bims-librar Biomed News
on Biomedical librarianship
Issue of 2026–07–19
57 papers selected by
Thomas Krichel, Open Library Society



  1. Intern Med J. 2026 Jul 17.
      The History of Medicine Library at the Royal Australasian College of Physicians is now one of the leading comprehensive research collections on the history of medicine of Australasia. This is a far cry from the founding fellows' original proposition in 1938, when they felt the need for a 'scientific library' to ensure the academic standards of their fledgling college. The present library has been made possible by donations from many personal and corporate collections. The first was a gift from the Royal College of Physicians of London in 1954, which redirected the focus to the history of medicine, followed in 1978 by the first munificent gift from Sir Edward Ford of his personal collection of Australiana. In the concluding decades of the 20th century, the value of all traditional history of medicine libraries worldwide was brought into question. Our library has survived the threat of dispersal on several occasions. The library and the extensive heritage collection of archives, artefacts, artwork and an image library have adapted to the challenges of the digital age, with greater visibility and accessibility through digital platforms. Our library, with its own unique history, has never looked so vibrant.
    Keywords:  History of Medicine Library; Leslie Cowlishaw; Royal Australasian College of Physicians; Sir Edward Ford
    DOI:  https://doi.org/10.1111/imj.70545
  2. J Med Libr Assoc. 2026 Jul 01. 114(3): 184-190
      This profile offers a portrait of Heather Nadean Holmes, MLIS, AHIP, 2025-2026 Medical Library Association (MLA) President, as a leader defined by authenticity, courage, and deep commitment to the profession. Holmes, known for her candor and dedication to the profession, brings a career rooted in service, gained from her work in hospital, academic, and association environments, to the presidency. Her professional journey reflects a sustained commitment to evidence-based practice, research, and elevating librarians as essential partners in health care, education, and discovery. Holmes is widely recognized for her work as a clinical medical librarian and her national leadership in developing the MLA Research Agenda. Colleagues describe her as a truth-teller who leads with integrity, sets clear boundaries, and consistently uses her platform to support others, especially early- and mid-career professionals. Her presidency focuses on inclusivity, transparency, and member engagement during a significant transition for both the Association and the profession. Beyond titles and accomplishments, this biography captures Holmes&s humanity. Her resilience, mentorship, humor, and unapologetic passions, including her love of K-pop, are highlighted. Together, these qualities portray a president who leads with conviction, empathy, and joy.
    Keywords:  Library Leadership; MLA; MLA President; Medical Library Association; Presidential Biography
    DOI:  https://doi.org/10.5195/jmla.2026.2417
  3. J Med Libr Assoc. 2026 Jul 01. 114(3): 306-314
       Background: Black, Indigenous, and People of Color (BIPOC) are underrepresented in librarianship. We developed a curriculum to introduce high school students from BIPOC communities to careers in health sciences librarianship and to concepts and skills in health information literacy.
    Case Presentation: Librarians at Weill Cornell Medicine partnered with Mentoring in Medicine, a Bronx-based nonprofit, to develop the Community Health Ambassador Program (CHAmP) curriculum. The project settings were at three New York City schools. The project took place over two years, with the pilot taking place in year 1, followed by an assessment which included data from pre- and posttests measuring student learning and focus groups with the students to gather feedback about their experience. Informed by data from the year one assessment, we worked with an experienced high school teacher to redesign the curriculum for year 2. Changes included reducing the amount of lecture and providing more time for activities to reinforce the content, including a team final project and presentations. In year two focus groups, students demonstrated increased understanding of health sciences librarianship and indicated increased engagement and enjoyment of the course compared to year 1. There was a statistically significant improvement in the mean student score from the pretest to the posttest.
    Conclusions: The revised curriculum resulted in increased student engagement and statistically significant improvement in learning compared to the year one pilot. We have published the full curriculum online under a Creative Commons license so that other organizations may implement it in their communities.
    Keywords:  Careers; High Schoolers; Literacy; New York; New York City
    DOI:  https://doi.org/10.5195/jmla.2026.2354
  4. J Med Libr Assoc. 2026 Jul 01. 114(3): 266-277
       Objective: Through a content analysis of public websites, this paper provides a centralized and summarized picture of evidence synthesis (ES) service characteristics at academic libraries and medical schools and centers in the United States in the mid-2020s.
    Methods: Our team identified 116 institutions classified in the 2021 Carnegie Classification as R1, R2, or Medical Schools and Centers that had an ES service. These services were coded for service characteristics. Using frequency and comparison tables, we answered the following research questions: What are the common structures and characteristics of ES services, within and independent of Carnegie Classification of Research Activity? Is there any relationship between Carnegie Classification of Research Activity and Size & Setting and characteristics of ES services?
    Results: We found that services often used tiers to delineate between services offered, with higher tiers requiring more responsibilities from the library or information professional. Some universities and medical schools limit librarian involvement with ES projects based on user role (student, faculty, researcher, etc.) or user affiliation with a college or department. Librarian acknowledgment or authorship are common in return for engagement in ES projects. Fees and Memoranda of Understanding were uncommon but do exist at some institutions.
    Conclusion: Librarians seeking to create or update their ES service can benefit from this 'data snapshot,& as it will allow them to see the service elements under consideration by other institutions and offer ideas for their own services. Overall, we found that there were more similarities than differences between basic ES service structures (e.g. tier amounts/categories, presence of fees, etc.), independent of classification type or size setting. We recommend that librarians utilize this paper and the associated data in order to identify which institutional elements are important in their context for ideas in improving their ES service.
    Keywords:  Content Analysis; Evidence Synthesis; Library Services
    DOI:  https://doi.org/10.5195/jmla.2026.2263
  5. J Med Libr Assoc. 2026 Jul 01. 114(3): 255-265
       Background: The study seeks to identify the board exam preparation (prep) resources made available to medical students by academic health sciences libraries and other departments within U.S. colleges of osteopathic medicine (COM) programs, and the extent to which these resources are utilized by medical students and educators.
    Methods: The study was conducted in two phases. In phase one, forty-two COM library directors were invited to complete a survey regarding their usage, funding, and access models for available board prep resources. In phase two, semi-structured interviews were conducted via Zoom to explore the library's role in providing access to these resources.
    Results: In phase one, thirty-five out of a possible forty-two survey respondents shared the top board exam prep resources to which their COM programs subscribed, as well as the top library resources that included board prep elements. In phase two, thirteen directors participated in Zoom interviews, which were recorded and transcribed to identify key trends in funding responsibility, resource selection, and librarian involvement. It was revealed that governance matters, digital format rules, partnerships drive impact, telling the data story is key, and one-stop guidance works.
    Conclusion: The two-phase study provided a comprehensive review of the specific resources that academic health sciences libraries and other departments offer for board prep within COM programs. Through analyses of survey and semi-structured interview data, this study identifies trends in resource availability and use, informing strategic planning for libraries, medical school administration, and library science education, ultimately enhancing student outcomes.
    Keywords:  Academic Health Sciences Libraries; Board Exam Preparation; Library Collection Development; Medical Libraries; Osteopathic Medicine; Undergraduate Medical Education
    DOI:  https://doi.org/10.5195/jmla.2026.2320
  6. J Med Libr Assoc. 2026 Jul 01. 114(3): 208-221
       Objectives: This study examines users& behaviors and perceptions when accessing medical information in digital environments and proposes strategic directions for medical libraries seeking to adopt emerging technologies. Specifically, we empirically investigate user expectations regarding digital technologies, differences in perception based on individual characteristics, and key factors influencing satisfaction with information use.
    Methods: We conducted a web-based survey from January to February 2024 through the National Center for Medical Science Knowledge, a national institution in South Korea. A total of 580 participants-including healthcare professionals, researchers, educators, and students-completed a questionnaire assessing digital information behavior, preferences for emerging technologies, perceptions of information and service quality, and overall satisfaction. We analyzed the data using descriptive statistics, analysis of variance (ANOVA), and multiple regression analysis.
    Results: Users exhibited a strong tendency to use online-based medical information, reporting particular interest in big data (38.5%) and artificial intelligence (24.9%) technologies. Statistically significant differences in preferences and awareness of digital technologies were observed based on gender, education level, academic major, and occupation. Information reliability (β = .623), service speed, and personalization were identified as key determinants of user satisfaction.
    Conclusion: Medical libraries should establish technology-based services that are suitable for the digital information environment and develop customized strategies that accurately reflect user characteristics and demands. This study offers practical insights for designing user-centered digital medical information services.
    Keywords:  Artificial Intelligence; Digital Health information; Emerging Technologies; Information Quality; Medical Libraries; User Satisfaction
    DOI:  https://doi.org/10.5195/jmla.2026.2314
  7. Front Psychol. 2026 ;17 1794392
      Guided by the Theory of Motivated Information Management (TMIM), this study examines how Chinese incoming first-year university students (N = 417) manage pre-enrollment uncertainty through information seeking. Data were analyzed using structural equation modeling. Results show that uncertainty discrepancy positively predicts anxiety. Anxiety was positively associated with outcome expectancy but negatively associated with efficacy expectancy. Both cognitive appraisals significantly predict information-seeking behavior. Furthermore, information overload serves as a moderator that strengthens the relationship between anxiety and outcome expectancy. Finally, consumption-oriented new media literacy exerts differentiated moderating effects: it enhances the impact of efficacy expectancy on information seeking while reducing the influence of outcome expectancy. By integrating information overload and consumption-oriented new media literacy into the TMIM framework, this study reveals how incoming first-year university students manage uncertainty in complex information environments.
    Keywords:  incoming first-year university students; information overload; information seeking; new media literacy; theory of motivated information management (TMIM)
    DOI:  https://doi.org/10.3389/fpsyg.2026.1794392
  8. J Med Libr Assoc. 2026 Jul 01. 114(3): 315-322
       Background: Research data services (RDS) have expanded in academic libraries but can be challenging to develop, particularly in teaching-intensive and less-resourced institutions. Learning communities offer a promising model for building skills, fostering collaboration, and aligning services with local needs.
    Case Presentation: This case report describes the development and implementation of three learning communities-a library group, a faculty group, and a student group-at a teaching-focused institution. These communities brought together library professionals, faculty, and students from diverse disciplines-including health sciences, education, data science, and engineering-to collaboratively explore the All of Us dataset. By working with the same dataset, participants were able to move quickly from abstract concepts to hands-on practice, while developing a shared understanding of tools, workflows, and challenges. The learning communities also served as platforms for building institutional capacity in data-intensive research.
    Conclusions: The learning communities model proved to be an effective strategy for fostering cross-disciplinary collaboration, promoting data literacy, and building institutional readiness to support research using the All of Us dataset. By centering on local expertise, learning communities provide a sustainable, resource-conscious framework for developing RDS. This approach also demonstrates how academic libraries can act as conveners and catalysts for equitable data engagement. Lessons learned from this case may inform similar efforts at other institutions seeking to build collaborative, inclusive models for engaging with various data resources.
    Keywords:  All of Us; Data Services; Group Learning; Interdisciplinary Collaboration; Learning Communities; Less-resourced Institutions; Teaching-focused Institutions
    DOI:  https://doi.org/10.5195/jmla.2026.2335
  9. J Med Libr Assoc. 2026 Jul 01. 114(3): 297-305
       Background: Fulfilling a recognized need in data skills training for academic librarians, the National Library of Medicine and the National Institutes of Health All of Us Research Program collaborated to enhance academic library workers& skills in biomedical and public health data, as well as their library's research capacity, through the awareness and use of the All of Us Researcher Workbench.
    Case Presentation: The All of Us Data Training and Engagement Program for Academic Libraries (ALP) blended professional development training, hands-on learning, and peer-to-peer networking that focused on increasing knowledge of the All of Us Researcher Workbench. Activities were designed to build institution-wide, interdisciplinary awareness and interest in using the All of Us Researcher Workbench. A series of ongoing activities ensured sustained skill-building and collaboration, and included training for R, a program language used for statistical computing and data visualization. Program activities were intentionally designed to help grow institutional research capacity, enhance skills in biomedical and public health data, and promote meaningful use of the Researcher Workbench to campus communities.
    Conclusion: The ALP helped participants overcome barriers to data access and improve research infrastructure and successfully empowered 115 library professionals to leverage the All of Us Researcher Workbench for meaningful biomedical and public health research. Measured outcomes validate the success of the program and demonstrate how the ALP has positioned participating institutions for long-term success in biomedical and public health research. Institutions can build upon the foundation established through this case report to advance equitable, data-driven health research across academic landscapes.
    Keywords:  Academic Libraries; All Of Us Research Program; Biomedical Data; Data Access; Data Literacy; Data training, Biomedical Informatics; Data-Driven Research; NIH; Public Health Data; Research Capacity Building
    DOI:  https://doi.org/10.5195/jmla.2026.2324
  10. Database (Oxford). 2026 Jan 15. pii: baag039. [Epub ahead of print]2026
      Major research and implementation efforts have been devoted to indexing articles according to the major topics discussed, but much less effort to indexing their publication types and study designs (collectively, PTs). In this Perspective, we discuss how indexing PTs differs from topical Medical Subject Heading (MeSH) indexing and requires a different approach. Rather than focus on the technical aspects of machine learning-based indexing models, we emphasize the goals and purposes for which biomedical articles are indexed, and the surprisingly thorny question of how indexing systems should be evaluated. Topical Medical Subject Heading (MeSH) terms are assigned to articles that cover the major topics discussed; when more than one term is applicable, only the most specific term is assigned. In contrast, PTs are assigned to articles that have a given structure or use a particular design. To meet the needs of end users, particularly groups involved in evidence syntheses, PT indexing needs to be comprehensive and employ probabilistic goodness-of-fit prediction scores. Whereas existing National Library of Medicine (NLM) hierarchies place publication types and study design-related terms on separate trees from each other, we have created a unified hierarchy that permits more appropriate retrieval via automatic expansion. Automated PT indexing systems should allow users to input article records or full-text PDFs and receive scores in real time. This will offer consistent indexing across bibliographic databases, as well as preprints and unpublished manuscripts. Automated PT indexing systems, properly designed and implemented, hold the promise of greatly improving the retrieval of biomedical articles, saving substantial effort when writing evidence syntheses and benefiting other users as well. Database URL:  https://www.nlm.nih.gov/medline/medline_home.html.
    DOI:  https://doi.org/10.1093/database/baag039
  11. J Med Libr Assoc. 2026 Jul 01. 114(3): 323-329
      The Association of Vision Science Librarians (AVSL) is an international organization composed of professional librarians, or persons acting in that capacity, whose collections include vision-related materials and/or whose patrons include vision scientists. Since 1976, AVSL has defined standards for vision science libraries in all capacities, including optometry, ophthalmology, academia, industry, and beyond. As the most recent Standards for Vision Science Libraries was published in 2014, an AVSL member committee was formed in 2024 to review the Standards and consider what a revision should include. Over the course of several months, committee members collaborated with one another, held discussions with other AVSL members, and reviewed current literature. What resulted is a revised set of Standards that aims to address the significant changes that have taken place not only within the field of librarianship, but with the information needs of the vision science professions as well. These updated Standards place the focus on the librarian rather than the library, as many librarian positions that once focused solely on vision science are now expected to manage several disciplines. The aim of this revision is to serve as a starting point for any librarian working with vision science materials and/or serving vision scientist patrons who wishes to develop their knowledge base and skills in this particular field of health sciences librarianship.
    Keywords:  Association of Vision Science Librarians (AVSL); Collection Development; Professional Competence; Professional Core Competency; Standards; Vision Sciences; ophthalmology; optometry
    DOI:  https://doi.org/10.5195/jmla.2026.2270
  12. J Med Libr Assoc. 2026 Jul 01. 114(3): 246-254
       Objective: Previous research has found bullying present across the library profession, with documented instances in school, public, and university literature, but not in academic medical and hospital library literature. This study aims to determine the prevalence of workplace bullying among academic medical and hospital librarians.
    Methods: An anonymous survey was distributed to members of the Medical Library Association, the Association of Academic Health Sciences Libraries, and the Association of College & Research Librarians Health Sciences Interest Group containing the Short Negative Acts Questionnaire. Quantitative analysis was conducted using the Real Statistics Resource Pack software (Release 8.9.1).
    Results: One-hundred eighteen survey responses met inclusion criteria. 49.2% of the sample met the threshold for self-identifying as victims of workplace bullying. This was significantly higher than the prevalence among the general librarian population (40.1%) in 2017 (p = 0.029) and consistent with findings in 2022 (51.8%; p = 0.31). More participants perceived themselves as not experienced bullying (69%) than the prevalence rate found. Those who perceived themselves as victims identified colleagues as the most frequent perpetrators (35%), followed immediate superiors (22%) and other superiors/managers within the organization (25%).
    Conclusions: This study showed nearly half (49.2%) of academic medical and hospital librarians included in the sample experience bullying. These findings highlight the need for an increased understanding of bullying behaviors, education on prevention, and for library leaders to develop interventions to mitigate bullying in their institutions.
    Keywords:  Health Science Librarian; Hospital Librarians; Librarians; Medical Librarian; S-NAQ; Workplace Bullying; prevalence
    DOI:  https://doi.org/10.5195/jmla.2026.2244
  13. J Med Libr Assoc. 2026 Jul 01. 114(3): 191-207
       Objectives: To synthesize and map literature on automated indexing of the biomedical literature, with a focus on the Medical Text Indexer (MTI) at the National Library of Medicine (NLM). We review the drivers, benefits, and challenges of automated indexing, evolution of the MTI from 2000-2025, and impacts on information retrieval in MEDLINE.
    Methods: We conducted a scoping review following the JBI Manual for Evidence Synthesis and reported findings using PRISMA-ScR and PRISMA-S. We searched several bibliographic databases, key journals, conference proceedings, and grey literature sources, with no restrictions on language or study design. Eligible publications were published 2000-2025 and focused on MTI development. Screening, data charting, and thematic analysis were conducted by multiple reviewers.
    Results: We included 64 publications, with most originating from the United States (n=53, 83%) and five from Canada (8%). Study methods included evaluation or comparative studies (65%), qualitative descriptions (25%), and mixed methods (11%). MTI evolved from a rules-based recommendation tool in 2002 to the neural network-based MTIX in 2024. Despite numerous enhancements to the MTI, human curation remains necessary for approximately one-third of records to correct inaccuracies, capture missed concepts, and address errors arising from figurative language or algorithmic biases.
    Conclusions: This review synthesizes twenty-five years of MTI research (2000-2025). Despite reduced indexing times and a markedly improved algorithm, the MTIX has not yet achieved full equivalence to human indexing. Our findings suggest searchers should watch for algorithmic ambiguities in their MEDLINE searching and adapt accordingly. Health sciences librarians should work with stakeholders, including authors, to shape future algorithmic indexing methods, outputs, evaluation and research.
    Keywords:  Algorithmic indexing; Automated indexing; Medical Subject Headings (MeSH); Medical Text Indexer (MTI); National Library of Medicine (NLM); Neural Network, Search Filters; PubMed/MEDLINE Searching
    DOI:  https://doi.org/10.5195/jmla.2026.2406
  14. J Med Libr Assoc. 2026 Jul 01. 114(3): 290-296
       Background: Librarians providing feedback on Evidence-Based Medicine (EBM) assignments face time constraints. This case report describes implementing a semi-automated rubric - defined here as a tool that automatically calculates scores from grader-selected ratings while narrative feedback is still written by the instructor - to reduce grading time for an EBM capstone assignment for first-year medical students.
    Case Presentation: Two cohorts of approximately 100 students completed the same EBM assignment. The 2023 cohort received manual feedback, while the 2024 cohort received feedback using the semi-automated rubric. Grading time was logged for both cohorts. The rubric, adapted from a template, automatically calculates scores based on selected criteria, streamlining the feedback process.
    Conclusions: The semi-automated rubric reduced grading time by 30%, from an average of seven minutes per assignment to five minutes. This simple, adaptable intervention can help reduce educator workload, improve feedback timeliness, and enhance assessment consistency. While limited by its single-grader and single institution design, this case report offers practical insights for educators seeking to improve feedback efficiency in EBM and other disciplines.
    Keywords:  Assessment; Evidence-Based Medicine; Medical Education; Medical Librarianship
    DOI:  https://doi.org/10.5195/jmla.2026.2343
  15. J Intern Med. 2026 Jul 14.
      
    Keywords:  biomedical literature; citation errors; evidence synthesis; fabricated references; medRxiv; research integrity
    DOI:  https://doi.org/10.1111/joim.70139
  16. Int J Digit Humanit. 2026 ;8(1-2): 1-23
      The United Kingdom's Maritime Heritage records hold vital information on heritage assets and archaeological contexts key to addressing current research priorities. These records stand alone, rarely connecting, demanding an arduous and time-consuming process of investigation and cross-referencing for synthesis to occur. Challenges intensify due to the qualities of this data; they stem partly from legacy information not initially intended for heritage recording purposes. Complications also arise from the varied methods of recording, storing, and disseminating this information deployed over time. However, archaeologists now possess Named Entity Recognition (NER) and Natural Language Processing (NLP) tools with the potential to organize and search such fragmented databases. This paper explores a method to enhance and enrich these datasets by employing Few Shot learning techniques to perform Multiclass Text Classification. The Welsh National Monuments Record (NMR), a glimpse into the U.K.'s maritime heritage, has been utilized to test these approaches.
    Keywords:  Few-Shot learning; Maritime archaeology; Metadata enrichment; Named entity recognition
    DOI:  https://doi.org/10.1007/s42803-025-00117-5
  17. Arch Microbiol. 2026 Jul 11. pii: 482. [Epub ahead of print]208(10):
      Conversion of unstructured biomedical literature into structured knowledge for identifying cross-domain associations between biological entities remains a challenging task. SigMine is an automated pipeline constructed to mine biomedical literature to identify significantly associated biological entities. SigMine performs biomedical entity recognition from PMC articles using the EuropePMC Annotation API. Advanced entity recognition was performed using Python scripting, NCBI E-Utilities, and an n-gram algorithm followed by extensive data cleaning and mapping against standard databases. Statistical evaluation identified significantly co-occurring entities. The entire workflow was automated through a modular framework developed in Python v3.13 with a Tkinter-based Graphical User Interface. SigMine enhances usability while retaining the flexibility to use new dictionaries for annotation. SigMine was used to construct a literature-derived potential human Opportunistic Pathogens Database (OPathDb), housing 5,626 potential opportunistic pathogens significantly co-occurring with 1440 diseases and 7121 genes mined from 25,000 PMC articles. Additional annotation of 598 significantly co-occurring metabolites and 30 affected tissues is available for 3204 and 227 pathogens, respectively. OpathDb has a user-friendly query interface searchable by organism, disease, tissue, gene, protein and metabolite available at https://www.opathdb.cbsblab-nsut.in . Organism-entity associations can be visualized as weighted networks, with color-coded nodes and significance-scaled edges. Significant associations of opportunistic pathogens like Akkermansia mucinifila with colorectal cancer and Segatella copri with glucose intolerance can be identified through OpathDb. Through this database, the SigMine framework demonstrates conversion of unstructured text in vast and heterogenous corpora into standardized and well-organized information. Statistically inferred associations in OPathDb are potential candidates for clinical and experimental validation.
    Keywords:  Biomedical literature mining; Entity attribute co-occurrence; Opportunistic pathogens database; Statistical significance; Unstructured to structured text
    DOI:  https://doi.org/10.1007/s00203-026-05045-8
  18. Healthcare (Basel). 2026 Jul 01. pii: 1911. [Epub ahead of print]14(13):
      Background: The early or latent phase of labour (early labour) is a time when women feel unsupported and have limited access to quality midwifery support, often being advised to stay at home. As a result, women seek online information and often turn to hospital websites as a trusted source of this information. Women from underserved and marginalised groups may be particularly reliant on online information. The aim of this study was to systematically evaluate the availability, accessibility, content, and evidence base of online early labour information provided by UK hospitals, with a focus on inclusivity, and equity in information provision. Methods: A systematic search of NHS and HSC maternity websites across the UK (England, Scotland, Wales, and Northern Ireland) was undertaken to identify publicly available guidance on early labour. Eligible materials included webpages, downloadable leaflets, and multimedia resources. The identified guidance was evaluated in terms of availability, accessibility, content, and transparency of evidence. Data were synthesised descriptively and presented using narrative summaries and tables. Results: A total of 146 hospital websites were reviewed, of which 72 (49%) provided guidance specific to early labour or included a dedicated section on the latent phase. There was marked variation in availability, accessibility, and content. Accessibility was often limited, with few multilingual resources, alternative formats, or inclusive visual materials. Most guidance was text-heavy, with minimal use of multimodal or user-friendly formats and limited representation of diverse populations. Clinical content also varied, particularly in definitions of early labour and recommendations for pain management. Only a minority of resources referenced supporting evidence. Conclusions: Online early labour information provided by UK maternity services varies in availability, accessibility, and inclusivity, raising important equity concerns. Limitations in accessibility, consistency, and transparency of evidence may contribute to disparities in understanding and decision-making, particularly among women from disadvantaged or marginalised groups. There is a clear need for standardised, evidence-based, and inclusive information that is accessible to diverse populations to support equitable maternity care during early labour.
    Keywords:  accessibility; early labour; equity; inclusivity; latent phase; maternity care; patient information
    DOI:  https://doi.org/10.3390/healthcare14131911
  19. J Med Libr Assoc. 2026 Jul 01. 114(3): 238-245
       Objective: Access to historic individually identifiable health information has been studied from a researcher perspective, but there has not been a comprehensive review of access policies at an institutional level to see if there is any consistency across institutions. The objective of this study is to gather preliminary information on these access policies and potentially identify similarities and gaps to inform future research.
    Methods: This study employs an online survey for data collection about respondents' access policies and procedures at their institution. The final survey instrument consisted of 29 questions with a mix of multiple choice and free-text questions. The survey was distributed through listservs and posted in forums that targeted qualified participants.
    Results: The survey received 36 responses, 21 of which met the criteria for inclusion in analysis. Some notable results are that covered entities are nearly equally split on when to open research collections, non-covered entities have a wide range of access policies, and all respondents were nearly equally split on how long it has been since they last updated their access policy.
    Conclusion: While the number of responses was low for this study, the results identified areas that would benefit from further research and more robust study methods to potentially achieve a more standardized approach to access policies at research institutions.
    Keywords:  Access to Information; Historical Research; Medical Collections; Medical Humanities; Privacy
    DOI:  https://doi.org/10.5195/jmla.2026.2259
  20. Curr Med Res Opin. 2026 Jul 16. 1-5
      Introduced in 2020, plain language summaries of publications (PLSPs) are standalone articles that aim to expand the reach of scientific findings to non-specialist audiences using plain language and meaningful visuals. Despite increased publication, there is no agreed cross-publisher definition of PLSPs, resulting in inconsistent terminology, formatting, peer-review processes, and metadata that hinder indexing and discoverability in bibliographic databases such as PubMed and Europe PMC. In February 2024, the multistakeholder collaboration Open Pharma launched an initiative to review existing standards and practices related to the publication of PLSPs. After identifying three publishers offering standalone PLSPs - Becaris Publishing, Sage, and Taylor & Francis - representatives from each formed a steering committee to define PLSPs and agree on principles that differentiate them from other publishing formats. PLSPs are defined as standalone summaries of peer-reviewed articles, written according to plain language principles, published open access with a unique digital object identifier (DOI), and are themselves peer reviewed (including by someone who is not a scientific specialist in the topic area). They should include a summary, clear reference to the original article, and a combination of text and meaningful graphics. This consensus provides a foundation for standardizing PLSPs as a distinct article type and acceptable secondary publication format. We hope it will also support consistent authoring, editorial management, peer-review processes and indexing. We call on indexing services to formally recognize PLSPs and on publishers to adopt this format as an accurate and reliable source of understandable scientific information.
    Keywords:  Plain language summary; accessibility; discoverability; medical writing; open access publishing; publishing; scholarly communication; standard
    DOI:  https://doi.org/10.1080/03007995.2026.2700975
  21. J Med Internet Res. 2026 Jul 16. 28 e90364
       Background: Patients increasingly use large language models (LLMs) to obtain medical information, but the quality of LLM-generated information on complex spinal infections such as spondylodiscitis remains uncertain. Existing evaluations in spine surgery have mainly addressed degenerative conditions or surgical procedures, and disease-specific data for spondylodiscitis are limited.
    Objective: This preliminary study evaluated spine surgeons' ratings of single-turn LLM responses to 10 author-curated frequently asked questions (FAQs) about spondylodiscitis and compared answer sets generated from GPT-4, GPT-4o, and Google Gemini web interfaces under the authors' implemented prompting conditions.
    Methods: A pool of patient-oriented questions was generated through a chronological workflow including publicly available FAQ sources, PubMed-informed terminology review, Google Trends topic checking, and LLM-generated candidate questions. Duplicate and semantically overlapping questions were removed, the remaining questions were grouped into thematic categories, and 10 final FAQ-style prompts were synthesized. Each prompt was submitted once to the publicly accessible web interfaces of GPT-4, GPT-4o, and Google Gemini. The study was interpreted as an expert evaluation of the resulting answer sets. Seven blinded board-certified spine surgeons rated the responses using a 4-level rating system ranging from excellent to unsatisfactory and additionally assessed comprehensiveness, clarity, empathy, and appropriateness of length. Descriptive statistics and nonparametric comparisons were performed. Interrater reliability was assessed using the intraclass correlation coefficient.
    Results: Across all responses, 38.6% (81/210) were rated as excellent, 39% (82/210) as satisfactory with minimal clarification needed, 16.7% (35/210) as satisfactory with moderate clarification needed, and 5.7% (12/210) as unsatisfactory. The most common reason for necessary clarification was insufficient information (58/141, 41.1%), followed by language-related issues (21/141, 14.9%) and overly detailed responses (18/141, 12.8%). The complication-related question received the highest mean rating (3.4/5), whereas treatment- and prognosis-related questions received lower ratings (2.7/5 and 2.9/5). Median overall ratings did not differ significantly among the 3 evaluated LLMs. Spine surgeons reported a generally positive attitude toward artificial intelligence-supported patient information but expressed remaining uncertainty regarding reliability and direct patient-physician communication.
    Conclusions: In this preliminary expert evaluation, selected publicly accessible LLM web interfaces generated mostly satisfactory responses to author-curated spondylodiscitis FAQs. The findings reflect outputs produced at the time of access under the authors' implemented prompting conditions. LLMs may support patient education only with clinician oversight. Future research should explore advanced, domain-specific models to further improve the quality of communication between clinicians and patients.
    Keywords:  AI; artificial intelligence; large language models; patient education; spine surgeons; spondylodiscitis
    DOI:  https://doi.org/10.2196/90364
  22. Digit Health. 2026 Jan-Dec;12:12 20552076261467813
       Background: Pulmonary tuberculosis (TB) is a chronic infectious disease that burdens patients and public health systems. Limited reach of traditional education and uneven online information may undermine patients' understanding, adherence, and trust. Large language models (LLMs) show promise for TB health education, but systematic evaluation is lacking.
    Objective: To evaluate five large language models in pulmonary tuberculosis Q&A (Question and Answer) scenarios and examine the effects of different large language models and TB health education themes on response quality, readability, and reliability, thereby supporting standardized artificial intelligence (AI)-assisted health education.
    Methods: This cross-sectional study was conducted from October 5 to October 11, 2025. Twenty pulmonary tuberculosis-related questions were developed with the assistance of a respiratory physician and categorized into five themes. The questions were entered into five large language models (Doubao, DeepSeek, Wenxin Yiyan, Tongyi Qianwen, and ChatGPT) to generate 100 text responses. Quality was evaluated using patient-education suitability (C-PEMAT-P) and Global Quality Score (GQS), and readability using seven indices, including Automated Readability Index (ARI) and Flesch Reading Ease Score (FRES). Statistical analyses included One-way analysis of variance (One-way ANOVA), Kruskal-Wallis, and correlation analysis.
    Results: GPT-5 achieved the highest C-PEMAT-P scores, followed by Doubao, while GQS scores were similar across models. Models showed significant differences on several readability indices, whereas themes had limited effects. Quality indicators were modestly associated with readability, while readability indices were strongly intercorrelated.
    Conclusions: Model type is a key determinant of TB health education text quality. Quality and reading difficulty are related but relatively independent and should be jointly considered when selecting large language model (LLM)-generated materials. Further studies should include more models, diseases, and patient-reported outcomes to optimize AI-assisted health education.
    Keywords:  LLM; artificial intelligence; online medical education; pulmonary tuberculosis
    DOI:  https://doi.org/10.1177/20552076261467813
  23. Caries Res. 2026 Jul 11. 1-17
       INTRODUCTION: Large language models (LLMs) have recently been integrated into dental practice to support clinical reasoning and preventive decision-making. This study compared the performance of five advanced chatbots-ChatGPT-5, Claude 4.5 Sonnet, Gemini 2.5 Pro, LLaMA 3.1, and Mistral 7B-in providing evidence-based responses for caries risk assessment and preventive management in pediatric cases.
    METHODS: Twenty-five validated, case-based questions were developed in accordance with internationally recognized pediatric and preventive dentistry guidelines. Responses were evaluated by six pediatric dentistry experts for accuracy, completeness, relevance, clarity, and usefulness using Likert-type scales. Response time, word count, and linguistic readability characteristics (Flesch Reading Ease Score and Flesch-Kincaid Grade Level) were additionally analyzed to compare textual complexity across chatbot-generated responses. Data normality was assessed using the Shapiro-Wilk test; parametric tests (ANOVA with Bonferroni correction) or non-parametric tests (Kruskal-Wallis with Dunn's post hoc) were applied as appropriate.
    RESULTS: Statistically significant differences were observed across all qualitative criteria, including accuracy, completeness, relevance, clarity, and usefulness (p < 0.001). ChatGPT-5 consistently ranked among the top-performing models, showing balanced and high-quality responses across domains, while Claude 4.5 Sonnet achieved the highest accuracy and completeness scores. Gemini 2.5 Pro produced the fastest responses (p < 0.001), whereas Claude 4.5 Sonnet generated the longest and most linguistically complex outputs. Readability metrics also differed significantly among models (p < 0.001), with Mistral 7B and LLaMA 3.1 showing the highest readability.
    CONCLUSIONS: All evaluated chatbots generated generally relevant responses for caries risk assessment and preventive counseling; however, substantial inter-model differences were observed in qualitative performance, linguistic complexity, and response characteristics. Occasional inconsistencies and outdated content highlight the need for cautious interpretation and further externally validated evaluation before broader clinical implementation.
    DOI:  https://doi.org/10.1159/000553557
  24. Int Urogynecol J. 2026 Jul 11.
       INTRODUCTION AND HYPOTHESIS: The application of artificial intelligence tools such as ChatGPT in patient education is expanding rapidly. Female stress urinary incontinence (SUI) is a common yet often overlooked urological condition. However, the accuracy, comprehensiveness, and evidence-based reliability of ChatGPT's responses to common SUI-related questions remain unclear.
    METHODS: On the basis of the AUA/SUFU clinical practice guidelines, 22 frequently asked questions regarding female SUI were developed and input into ChatGPT-3.5 and ChatGPT-4, respectively. Two senior urologists independently evaluated the responses using a 5-point Likert scale for accuracy, comprehensiveness, and relevance. Additionally, the word count of each response was recorded, and the validity of any cited references was verified.
    RESULTS: A total of 44 AI-generated responses were analyzed for the 22 SUI-related questions. Both ChatGPT-3.5 and ChatGPT-4 provided high-quality medical information, with accuracy scores of 4.09 and 4.45, respectively (p = 0.097). ChatGPT-4 offered significantly more concise responses (200.55 ± 48.21 words) compared to ChatGPT-3.5 (515.68 ± 198.13 words; p < 0.001). Furthermore, ChatGPT-4 demonstrated a significantly higher proportion of valid citations (72.06% vs. 24.27%, p < 0.001).
    CONCLUSIONS: ChatGPT-4 demonstrated strong performance in delivering accurate, concise, and evidence-supported information on female SUI. Future research should expand the scope of evaluation, incorporate patient perspectives, and validate the practical utility and safety of AI tools in real-world clinical settings.
    Keywords:  Artificial intelligence; ChatGPT; Citation validity; Information quality; Stress urinary incontinence
    DOI:  https://doi.org/10.1007/s00192-026-06685-6
  25. BMC Oral Health. 2026 Jul 14.
       BACKGROUND: Artificial intelligence (AI) chatbots are being increasingly used to obtain health-related information; however, the quality, completeness, readability, and consistency of AI-generated responses across different languages remain insufficiently investigated in endodontics, particularly regarding the management of deep caries and pulp exposure.
    METHODS: Thirty open-ended questions were developed on the basis of the 2019 European Society of Endodontology (ESE) position statement and the current literature. The questions were submitted in English and Turkish to ChatGPT-4o, ChatGPT-5, Consensus Pro, and Perplexity Pro. The responses were independently evaluated by two blinded endodontists via the modified DISCERN (mDISCERN), the Global Quality Score (GQS), and a three-point completeness scale. Readability was assessed via the Flesch Reading Ease Score (FRES) for English responses and the Ateşman Readability Index for Turkish responses. Data were analyzed using two-way analysis of variance (ANOVA), intraclass correlation coefficient (ICC) analysis, and post hoc multiple comparisons.
    RESULTS: AI chatbots and language significantly affected readability and response time (p < 0.05). Turkish responses demonstrated higher readability scores, particularly for the ChatGPT-4o and ChatGPT-5. Perplexity generated the shortest response times, whereas Consensus had the longest response times. The quality scores (mDISCERN and GQS) were significantly influenced by the AI model but not by language. Consensus achieved the highest quality ratings in both languages.
    CONCLUSIONS: AI model selection had a greater influence on response quality than language did. Consensus achieved the highest expert-rated quality and completeness scores, whereas Perplexity generated the fastest responses. These findings highlight the importance of carefully selecting AI tools when seeking evidence-based endodontic information across different languages.
    Keywords:  Artificial intelligence; ChatGPT; Consensus; Deep caries; Perplexity; Pulp exposure
    DOI:  https://doi.org/10.1186/s12903-026-09298-z
  26. Proc (Bayl Univ Med Cent). 2026 Jul 15. 1-4
       BACKGROUND: Metformin, lisinopril, and atorvastatin rank among the most commonly prescribed medications for Medicaid patients; however, patients often have inadequate knowledge regarding their safe usage. Generative artificial intelligence (AI) tools are becoming a prevalent source of medication information, but their readability for low-literacy populations compared to authoritative drug information sources lacks empirical assessment.
    METHODS: Sixteen standardized medication counseling inquiries (5 for metformin, 5 for lisinopril, and 6 for atorvastatin) were presented to three AI platforms (ChatGPT, Google Gemini, and Meta Llama 4) under default and sixth-grade-prompted conditions, as well as to six pharmaceutical information websites (FDA.gov, MedlinePlus, Drugs.com, Mayo Clinic, WebMD, and RxList). The Flesch-Kincaid Grade Level (FKGL) was calculated automatically from extracted passages utilizing Python textstat v0.7.3. Identical normalized-text duplicates within each source folder were omitted (84 exclusions; final unique source count = 12). The Mann-Whitney U and Wilcoxon signed-rank tests evaluated FKGL disparities (α = 0.05).
    RESULTS: The average pooled default AI FKGL was 12.19 ± 4.85, while the pooled sixth-grade AI FKGL was 7.63 ± 3.16. The pooled website FKGL after deduplication was 10.49 ± 3.95 (Mann-Whitney U = 343.0, P = 0.31, rank-biserial r = -0.19).
    CONCLUSION: The AI-generated medication counseling passages in this sample frequently exhibited a higher FKGL than the aggregated website passages; sixth-grade prompting markedly decreased FKGL across all three models (all P ≤ 0.001). This article exclusively addresses readability. Pharmacists and prescribers should endorse reputable websites that provide suitable assistance; grade-level prompts may facilitate patient comprehension of AI-generated summaries.
    Keywords:  Artificial intelligence; Medicaid; health literacy; large language models; medication counseling; readability
    DOI:  https://doi.org/10.1080/08998280.2026.2703446
  27. Acad Pediatr. 2026 Jul 14. pii: S1876-2859(26)00180-4. [Epub ahead of print] 103398
       OBJECTIVE: Since the 1994 "Back to Sleep" campaign, pediatricians have promoted evidence-based infant safe sleep practices to reduce sleep-related infant deaths. However, caregivers increasingly seek guidance online. We sought to determine the accuracy of large language model (LLM) responses to caregiver questions about infant safe sleep, compared with the American Academy of Pediatrics' (AAP) 2022 recommendations.
    DESIGN: Nine caregiver questions adapted from Reddit New Parents forum were mapped to core AAP safe sleep topics. Each was entered into three LLMs: ChatGPT 5, Gemini 2.5 Flash, and Claude Sonnet 4.5, three times within the same day to assess stability. Three reviewers scored responses on a 0-2 scale for accuracy (primary outcome), completeness, and empathy. Stability reflected similarity across repeated responses. Readability was calculated using the Flesch-Kincaid grade level. Mean scores were compared using descriptive statistics, analysis of variance, and post hoc testing.
    RESULTS: Mean accuracy varied significantly across models. Gemini had the highest accuracy score (mean 1.85), followed by Claude (1.44), and ChatGPT (1.30). Gemini was significantly more accurate than ChatGPT (p=0.01). All models scored high in empathy (2). There were no significant differences in completeness and stability between models. ChatGPT had the lowest average readability, with all models' reading levels between grades seven to nine (7.64 vs 9.15 vs 8.82, p=0.01). Direct guideline questions yielded higher accuracy than nuanced questions.
    CONCLUSION: LLMs offer inconsistently accurate but empathetic infant safe sleep advice with frequent deviations from AAP recommendations. Pediatric oversight and collaboration with technology developers are essential to ensure safe, evidence-based information for families.
    Keywords:  artificial intelligence; infant safe sleep; injury prevention; patient/family education
    DOI:  https://doi.org/10.1016/j.acap.2026.103398
  28. Front Public Health. 2026 ;14 1864894
       Objectives: Generative artificial intelligence (generative AI) has been investigated for creating patient education materials (PEMs) to reduce the burden of clinical education and improve health information accessibility. However, prior studies have highlighted limitations in readability, content stability, source transparency, and generation quality. The release of ChatGPT-5.4 provides an opportunity to evaluate the inter-generation stability and usability of a newer frontier model. This study evaluates ChatGPT-5.4's performance in producing PEMs for spinal surgery.
    Method: On 5 March 2026, ChatGPT-5.4 was used to address common patient questions about three prevalent spinal surgeries: lumbar disc herniation surgery, spinal fusion surgery, and spinal decompression surgery. Each question generated five independent responses. Qualitative analysis evaluated language sophistication, information depth, structural clarity, and supplementary content. Readability was measured using the Flesch-Kincaid Reading Ease (FKRE), Flesch-Kincaid Grade Level (FKGL), and Simple Measure of Gobbledygook (SMOG). Quality was assessed by two independent reviewers using the DISCERN tool, with the Intraclass Correlation Coefficient (ICC) calculated.
    Results: Core medical information remained consistent across generated versions for all three question types, although variations occurred in tone, organization, and detail presentation. The average FKRE ranged from 43.02 to 57.16 ("difficult" to "standard English"), FKGL from 9.12 to 13.34, and SMOG from 8.90 to 11.84, corresponding to reading levels from advanced junior high to early university. The average DISCERN score ranged from 43.2 to 44.9 ("average" quality). ChatGPT-5.4 showed moderate-to-high inter-generation stability with limited content drift in this exemplar context. However because no earlier model was evaluated head-to-head under identical prompts, raters, and time points, this finding should be interpreted as within- study evidence of stability rather than evidence of improved stability over earlier models. Readability remained challenging, and verifiable references were absent.
    Conclusion: Within this exemplar spinal-surgery context, ChatGPT-5.4 demonstrated moderate-to-high inter-generation consistency in lexical content and core medical themes. Complex language and lack of traceable references may limit accessibility and patient trust.
    Practice implications: ChatGPT-5.4 may support spinal-surgery patient education by generating stable, clinically plausible PEMs, though factual accuracy was not independently verified. Readability remains above recommended health- literacy levels, and clinician review and plain-language optimization are required before patient use.
    Keywords:  ChatGPT-5.4; DISCERN; health information; large language model; patient education; readability; spinal surgery
    DOI:  https://doi.org/10.3389/fpubh.2026.1864894
  29. Medicine (Baltimore). 2026 Jul 17. 105(29): e49787
      This cross-sectional study aimed to evaluate the quality, understandability, actionability, and readability of patient-oriented information generated by ChatGPT regarding overactive bladder (OAB). A total of 32 frequently asked patient questions related to OAB were categorized into 6 domains, including general information, diagnosis, lifestyle and behavioral management, medical treatment, minimally invasive treatments, and surgical treatment, and submitted to ChatGPT-4o. The responses were evaluated independently by 2 researchers using the DISCERN instrument to assess information quality and the Patient Education Materials Assessment Tool - Printable version to evaluate understandability and actionability. Readability was assessed using the Flesch-Kincaid Grade Level and Simple Measure of Gobbledygook formulas. The overall mean DISCERN score was 51, indicating moderate information quality across categories. Patient Education Materials Assessment Tool analysis demonstrated relatively high understandability (81.8%) but limited actionability (45.5%). Readability analysis revealed that the responses exceeded recommended patient education standards, with overall median Flesch-Kincaid Grade Level and Simple Measure of Gobbledygook scores of 14.00 (range: 7.61-17.11) and 12.34 (range: 5.01-14.93), respectively. Category-based analyses demonstrated variability across content domains, with treatment-related responses showing relatively higher information quality and lifestyle-related responses demonstrating greater actionability. Although ChatGPT-generated responses demonstrated moderate information quality and were generally understandable, limitations related to actionability and readability may restrict their practical use in patient education. These findings suggest that ChatGPT may serve as a supportive tool for patient education in OAB; however, it should be considered a complementary resource used under physician supervision rather than a substitute for clinical guidance.
    Keywords:  ChatGPT; artificial intelligence; overactive bladder; patient education
    DOI:  https://doi.org/10.1097/MD.0000000000049787
  30. JMIR Diabetes. 2026 Jul 15. 11 e93822
       Background: Type 2 diabetes mellitus (T2DM) affects approximately 590 million people worldwide, and its management relies heavily on patient education. With the emergence of online health information and artificial intelligence (AI) large language models, patients are increasingly sourcing medical information independently.
    Objective: This study compared the quality, readability, and transparency of websites and AI-generated leaflets (AIGLs) related to T2DM.
    Methods: Four predefined search terms ("type 2 diabetes," "type 2 diabetes mellitus," "T2DM," and "adult diabetes") were entered into 3 major search engines (Google, Yahoo, and Bing), and the top 20 search results were retrieved. AIGLs with patient information about T2DM were produced using a standardized prompt in 4 AI large language models (ChatGPT, Gemini, DeepSeek, and Grok). Information quality was assessed using the DISCERN score, calculated by 3 independent raters and ChatGPT. The Journal of the American Medical Association (JAMA) benchmarks were used to measure reliability and transparency. The Flesch-Kincaid Grade Level was used to determine readability.
    Results: Seventy-five websites and 4 AIGLs were evaluated. Mean author-rated DISCERN scores were 42.6 (SD 11.3) for websites and 43.9 (SD 1.74) for AIGLs, corresponding to fair quality (DISCERN 41-51). In contrast, ChatGPT-rated mean DISCERN scores were higher, with 58.5 (SD 11.5) for websites and 61.0 (SD 2.94) for AIGLs, corresponding to good quality (DISCERN 52-63). Mean JAMA benchmark scores were 2.74 (SD 0.965) for websites, indicating moderate reliability (2-3 out of 4 points), whereas all AIGLs scored 0 out of 4 points. Mean Flesch-Kincaid Grade Level scores for websites were 8.67 (SD 2.23) and 8.30 (SD 1.92) for AIGLs, corresponding to an eighth- to ninth-grade comprehension level. Spearman rank correlation demonstrated minimal variability among the 3 independent raters but showed a significant difference between ChatGPT and the 3 independent raters.
    Conclusions: Given the high prevalence of T2DM, both websites and AIGLs demonstrated suboptimal quality, readability, and transparency. Increasing patient reliance on digital health information calls for improved readability standards and stronger safeguards for AI-generated content. Both websites and AIGLs require an eighth- to ninth-grade comprehension level, far above the average reading age of 9 years in the United Kingdom (fourth- to fifth-grade level). This reduces the accessibility of online health information. The landscape of medical consultations is evolving, with patients increasingly presenting with preconceived notions based on online health information; hence, health care professionals should adapt to this shift.
    Keywords:  AI; ChatGPT; artificial intelligence; digital health; online patient information; type 2 diabetes mellitus
    DOI:  https://doi.org/10.2196/93822
  31. Neurourol Urodyn. 2026 Jul 17.
       BACKGROUND: Nowadays, artificial intelligence Large Language Models (LLMs) are widely used by patients and physicians alike to investigate various medical topics. Pelvic floor rehabilitation has become a popular subject in recent years.
    OBJECTIVES: The aim of this study is to assess and compare the usability, readability, and repeatability of three LLMs - ChatGPT, DeepSeek and Gemini - in relation to pelvic floor rehabilitation.
    METHODS: A total of 35 questions derived from the three most frequently searched Google Trends keywords related to pelvic floor rehabilitation ('pelvic floor dysfunction', 'pelvic floor exercises', and 'pelvic floor physical therapy') were evaluated by two raters. The quality of the responses was assessed using the Brief DISCERN (BD), a validated tool for evaluating the quality of health information. Readability was assessed using the Flesch-Kincaid Reading Ease (FRE), the Flesch-Kincaid Reading Grade Level (FKRGL) and the Simple Measure of Gobbledygook (SMOG) index. Responses were evaluated at two separate time points to assess consistency.
    RESULTS: Statistically significant differences in the SMOG index were observed among the AI models at the first and second evaluations (p < 0.001), but only at the second evaluation for FKGL (p = 0.006). Significant differences were also observed between LLMs for BD scores for physical therapy section at both first and second evaluations (p < 0.001, p = 0.035 respectively).
    CONCLUSIONS: ChatGPT, DeepSeek and Gemini provided readable, useful and repeatable answers to questions related to pelvic floor rehabilitation. However, it is important to bear in mind that LLMs are supplementary tools.
    Keywords:  Artificial intelligence; ChatGPT; Deepseek; Gemini; consistency; pelvic floor rehabilitation
    DOI:  https://doi.org/10.1002/nau.70385
  32. JMIR Form Res. 2026 Jul 13. 10 e85196
       Background: Parents increasingly consult the internet, both websites and, more recently, artificial intelligence chatbots, for information on autism spectrum disorder (ASD). However, the comparative quality of these two source types, especially across languages, remains underexplored.
    Objective: This study aimed to assess the completeness and accuracy of ASD information delivered by websites and 5 popular artificial intelligence chatbots and determine whether performance differs between English and Romanian content.
    Methods: In a cross-sectional design, 25 English-language and 25 Romanian-language websites and the responses of ChatGPT, Gemini, Claude, Copilot, and DeepSeek were evaluated. Content was benchmarked against a 24-item checklist, yielding completeness and accuracy scores. Chatbots were tested in 2 scenarios: a single broad query (A) and 24 item-specific queries (B).
    Results: Websites achieved higher completeness in English than in Romanian (6.9 vs 5.1; P=.007) and marginally higher accuracy (6.9 vs 6.1; P=.045). In scenario A, chatbot completeness (English: 5.3 vs Romanian: 6.2; P=.15) and accuracy (English: 6.0 vs Romanian: 5.6; P=.32) did not show significant differences by language. In the single-query scenario, websites showed higher accuracy than chatbots in both English (6.9 vs 6.0; P=.19) and Romanian (6.1 vs 5.6; P=.53), with neither difference reaching statistical significance. Conversely, item-specific questioning favored chatbots, which yielded higher accuracy scores than websites in English (8.3 vs 6.9; P=.053) and Romanian (8.0 vs 6.1; P=.007). Accuracy scores improved significantly from the single-query to the item-specific scenario (English: 6.0 vs 8.3; P=.002; Romanian: 5.6 vs 8.0; P=.001). While an initial analysis suggested variation in performance between chatbots (repeated measures ANOVA; P=.005), pairwise differences between individual models did not remain significant after adjustment for multiple testing.
    Conclusions: This exploratory study indicates that the quality of online ASD information varies by language and source context. English-language websites are more complete than Romanian-language websites. Among chatbots, targeted questioning yields more accurate answers than single broad queries in both languages. The findings should be interpreted cautiously due to the temporal gap between website and chatbot data collection.
    Keywords:  autism spectrum disorder; chatbots; completeness and accuracy; information quality; large language models; multilingual assessment; online health information
    DOI:  https://doi.org/10.2196/85196
  33. Front Pediatr. 2026 ;14 1788952
       Introduction: Large language models (LLMs) are increasingly used to provide medical information, yet their performance in rare pediatric cancers remains largely unexplored. This study aimed to compare the clinical accuracy, comprehensiveness, and communication quality of five widely used LLMs in answering frequently asked questions about Ewing sarcoma.
    Methods: Twelve representative questions covering diagnosis, treatment, prognosis, and psychosocial support were presented to ChatGPT (GPT-5.2), Claude Sonnet 4.5, Gemini 3, DeepSeek V3.2, and Grok 4. Two orthopedic oncology specialists independently evaluated each response using a 4-point Likert scale assessing clinical accuracy, completeness, clarity, and relevance. Qualitative assessments of empathy and communication quality were also performed. Statistical analyses included the Friedman, Wilcoxon signed-rank, Kruskal-Wallis, and Mann-Whitney U tests.
    Results: Significant differences were observed among the five LLMs (p < 0.001). ChatGPT achieved the highest overall performance, followed by Claude and DeepSeek. DeepSeek demonstrated the greatest technical accuracy but lower communication quality, whereas ChatGPT provided the best balance between factual correctness and patient-friendly communication. Gemini and Grok produced more superficial responses with lower overall scores.
    Discussion: Current LLMs can support patient and family education in Ewing sarcoma but should not replace specialist consultation. Although ChatGPT and Claude demonstrated the most reliable overall performance, variability among models remains substantial. Further validation and disease-specific optimization are required before routine implementation in clinical practice.
    Keywords:  Ewing sarcoma; artificial intelligence (AI); large language models; orthopedic oncology; patient education; pediatric oncology
    DOI:  https://doi.org/10.3389/fped.2026.1788952
  34. Arch Esp Urol. 2026 Jun;79(5): 831-836
       BACKGROUND: This study investigated the quality and comprehensibility of responses generated by four different artificial intelligence (AI)-powered chatbots (ChatGPT, Gemini, DeepSeek, and Grok) when queried about testicular prostheses.
    METHODS: A Google search using the keyword "testicular prosthesis" was conducted, and the 50 most frequently asked questions listed in the "People Also Ask" section were identified. These questions were categorized into preoperative, perioperative, and postoperative topics and were posed to four AI chatbots: ChatGPT, Google Gemini, DeepSeek, and Grok. The responses were independently evaluated by four urologists using the Global Quality Scale (GQS), Modified DISCERN, and Patient Education Materials Assessment Tool for Printed Materials (PEMAT-P) scales to assess quality, reliability, and readability.
    RESULTS: According to the GQS evaluation, the median scores for ChatGPT, Gemini, DeepSeek, and Grok were 4.75, 4.5, 4.75 and 4.875, respectively (p < 0.001). According to the Modified DISCERN scale, the scores were 2.75, 3.0, 3.75 and 3.0, respectively. The PEMAT-P understandability scores were 79.1%, 75.0%, 84.2% and 78.6%, respectively (p < 0.001). Similarly, the PEMAT-P actionability scores were 73.5%, 68.5%, 78.9% and 73.2%, respectively (p < 0.001).
    CONCLUSIONS: While all chatbots provided high-quality responses, their reliability was moderate. DeepSeek demonstrated the best performance across the evaluated metrics. These findings suggest that AI chatbots may serve as useful supplementary tools for patient education regarding testicular prostheses.
    Keywords:  artificial intelligence; chatbot; patient education; quality assessment; testicular prosthesis
    DOI:  https://doi.org/10.56434/j.arch.esp.urol.20267905.97
  35. Sci Rep. 2026 Jul 17.
      This study investigates the readability, clinical reliability, and temporal consistency of artificial intelligence (AI) chatbots regarding pneumothorax information. A question bank comprising 40 patient-centered queries was deployed across three large language models (ChatGPT, Gemini, Copilot), stratified by two access tiers and two prompting strategies (zero-shot versus the optimized PROMPORT strategy). Queries were replicated longitudinally on Days 1, 3, and 7 under strict session-control protocols. Text accessibility was quantified using five automated readability indices, while two independent, blinded thoracic surgeons evaluated clinical quality using modified DISCERN (mDISCERN), JAMA benchmarks, and PEMAT-P indices. Readability metrics demonstrated absolute structural stability across the tracking intervals (p > 0.05). Unprompted configurations consistently generated complex, high-school-level outputs, whereas the PROMPORT strategy successfully compressed linguistic variances and neutralized chronological algorithmic drift (p > 0.05). Conversely, unprompted architectures exhibited significant temporal volatility in mDISCERN and JAMA profiles (p < 0.05), which was successfully stabilized by optimized prompt constraints. Inter-rater reliability was high across all structural evaluations. In conclusion, while unprompted models exhibit marked baseline linguistic and quality variations, the strategic integration of robust prompt engineering successfully enforces the temporal stability and clarity required for reliable digital public health communication.
    Keywords:  Artificial intelligence; Health literacy; Large language models; Pneumothorax; Thoracic surgery
    DOI:  https://doi.org/10.1038/s41598-026-62194-y
  36. Clin Anat. 2026 Jul 15.
      Large language models (LLMs) are increasingly used in medical education and academic writing. However, concerns remain regarding reference hallucination, citation, and the reliability of LLM-generated content. This study aimed to evaluate the performance of ChatGPT 5.2, Gemini 3 Pro, and DeepSeek V3.2 in generating anatomy-related responses by assessing bibliographic reference accuracy, citation content consistency, and the readability of LLM-generated content. A total of 120 open-ended anatomy questions covering six anatomical categories (neuroanatomy, musculoskeletal, respiratory and circulatory, gastrointestinal, urogenital and endocrine, head and neck) were submitted to each model. Individual citation components, including author names, article titles, journal names, publication details, and PMIDs, were verified against indexed sources. Citation content consistency was evaluated using a three-point Likert scale. Readability was assessed using the Flesch Reading Ease score, Flesch-Kincaid Grade Level, Coleman-Liau, and Simple Measure of Gobbledygook indices. A total of 1800 references were analyzed. ChatGPT 5.2 demonstrated the lowest hallucination rate (23.2%), whereas Gemini 3 Pro and DeepSeek V3.2 exhibited substantially higher hallucination rates (45.8% and 47.5%, respectively). DeepSeek V3.2 achieved the highest accuracy for several individual bibliographic components, including author names, article titles, volumes, issues, pages, and journal names. PMID accuracy remained limited across all models, ranging from 25.1% to 57.6%. Citation content consistency differed significantly among the models (p < 0.001), with ChatGPT 5.2 demonstrating the highest proportion of fully supported citations (67.2%), compared with Gemini 3 Pro (42.5%) and DeepSeek V3.2 (41.0%). Citation accuracy differed significantly across most anatomical subcategories, with the greatest intermodel discrepancy observed in head and neck anatomy. Readability analyses indicated that the generated responses generally required college-level reading proficiency. Although LLMs can generate plausible anatomy-related responses, substantial limitations remain regarding reference accuracy, hallucination, and citation reliability. Human verification remains essential before incorporating LLM-generated references into academic or educational materials.
    Keywords:  anatomy education; citation accuracy; large language models; readability; reference hallucination
    DOI:  https://doi.org/10.1002/ca.70187
  37. BMC Oral Health. 2026 Jul 17. pii: 1278. [Epub ahead of print]26(1):
       BACKGROUND: Large Language Model (LLM) -based chatbots are increasingly used in patient information processes. The aim of this study was to compare the performance of ChatGPT (GPT-5.1), Gemini (2.5 Flash), and Claude (Sonnet 4.5) with expert periodontologists in responding to periodontal questions. Responses were evaluated in terms of scientific accuracy, completeness, conciseness & focus, empathy, and clarity, and differences among groups were investigated.
    METHODS: The question pool was developed de novo based on clinical experience. The literature and online search trends were used to support content coverage. The questions were evaluated using Lawshe's content validity method, and 20 open-ended questions were included. Expert responses were prepared by three experienced periodontologists based on the literature and clinical guidelines. The questions were then submitted using a standardized prompt to ChatGPT (GPT-5.1), Gemini (2.5 Flash), and Claude (Sonnet 4.5), and the first responses were recorded. All responses were anonymized and evaluated by nine independent periodontologists using a 5-point Likert scale in terms of scientific accuracy, completeness, conciseness & focus, empathy, and clarity. Statistical analyses were performed using the Friedman test with Bonferroni-corrected post-hoc comparisons, and inter-rater agreement was assessed using the intraclass correlation coefficient.
    RESULTS: No significant difference was found among the groups in terms of scientific accuracy (p = 0.425). A significant difference was observed for completeness (p < 0.0001), with LLM-based chatbot responses achieving higher scores than expert responses. For conciseness & focus, a significant difference was found (p < 0.0001); Gemini demonstrated lower scores compared to the other groups, while no significant differences were observed among ChatGPT, Claude, and expert responses. A significant difference was observed for empathy (p < 0.0001), with all LLM-based chatbot responses scoring higher than expert responses. For clarity, a significant difference was found (p < 0.0001), with a difference observed only between ChatGPT and Gemini. Inter-rater agreement was within a good range across all domains.
    CONCLUSIONS: This study showed that LLM-based chatbots can generate responses with a level of scientific accuracy comparable to expert periodontologists. However, despite providing more comprehensive and empathetic responses, some models demonstrated limitations in terms of conciseness & focus and clarity. These findings indicate that model differences should be considered in patient education and information-seeking processes. Expert responses were able to present a similar level of scientific accuracy using fewer and more focused expressions. This may make it more difficult for patients to maintain focus and to perceive information in a structured manner when using AI-generated responses. Overall, AI systems may serve a supportive role in patient information processes; however, physician supervision remains necessary in clinical use. The appropriate integration of LLM-based chatbots into patient information processes may enhance patient communication and access to information, provided that they are used as supportive tools rather than independent decision-makers in clinical practice.
    Keywords:  Artificial intelligence; ChatGPT; Clarity; Empathy; Large language models; Patient education; Periodontology
    DOI:  https://doi.org/10.1186/s12903-026-09315-1
  38. Cureus. 2026 Jun;18(6): e110636
      Background Fluoride plays a critical role in public dental health, and its judicious use is central to caries prevention strategies worldwide. With the exponential growth of internet usage in the Arab world, dental websites in the Arabic language have become increasingly influential sources of health information. However, the accuracy, completeness, and scientific reliability of fluoride-related content on these platforms remain largely unexplored. This study aimed to assess and evaluate the quality and accuracy of fluoride information disseminated through Arabic-language websites. Methods A cross-sectional web-based study was conducted between January and April 2025. Arabic websites were systematically identified using Google, Bing, and Yahoo search engines with standardized Arabic search terms related to fluoride and dental health. Websites were evaluated using the DISCERN instrument, the Journal of the American Medical Association (JAMA) benchmark criteria, the Health on the Net (HON) code principles, and a custom fluoride-specific accuracy checklist developed from current evidence-based guidelines. A total of 120 websites met the inclusion criteria. Descriptive statistics, chi-square tests, and multivariate logistic regression were employed for data analysis. Results Of 120 eligible websites, 38 (31.7%) were classified as commercial, 29 (24.2%) as health information portals, 27 (22.5%) as governmental or academic, and 26 (21.7%) as social media-based or blog platforms. Overall website quality was rated poor to fair in 68.3% of assessed websites (n = 82). The mean DISCERN score was 38.4 out of 80 (SD = 11.2). Fluoride-specific accuracy scores revealed that only 44.2% of websites correctly described optimal fluoride concentration for drinking water, 52.5% accurately discussed fluoride toothpaste recommendations, and a mere 31.7% provided correct information regarding dental fluorosis. Governmental and academic websites demonstrated significantly higher quality scores compared to commercial and social media platforms (p < 0.001). Conclusion The majority of Arabic websites provide inadequate, inaccurate, or misleading fluoride-related health information. Significant quality disparities exist across different website categories, with governmental and academic websites outperforming commercial and social media platforms. There is an urgent need for standardized guidelines, regulatory oversight, and professional dental society engagement to improve the quality of fluoride information on Arabic dental websites.
    Keywords:  dental health information; discern; fluoride; fluorosis; internet health resources; website quality
    DOI:  https://doi.org/10.7759/cureus.110636
  39. JMIR Pediatr Parent. 2026 Jul 14. 9 e70630
       Background: Infants with complex heart disease often have delayed development, learning difficulties, and mental health problems as they grow older. Their parents and other caregivers engage in online health information-seeking behavior to understand and support their children's health and development.
    Objective: In this study, we analyze the presence, nature, and presentation of information about neurodevelopment on the websites of congenital cardiac surgical programs in the United States.
    Methods: We used a mixed methods approach, specifically a convergent design. We correlated the presence and presentation of information about neurodevelopment on each program's website with state-level and program-specific factors extracted from multiple publicly available databases. Quantitative analysis methods included descriptive analyses, Student t tests, Pearson chi-square test, and multivariate logistic regression analysis, all performed using SPSS (IBM Corp). Qualitative methods included both inductive and deductive content analysis.
    Results: Only 39% (50/129) of programs provided any information about neurodevelopment online. High surgical volume correlated with the presence of online information (P<.001). Websites were written at an average 7th to 10th grade reading level, and fewer than 5% of websites had information in a language other than English. Two semantic clusters of website format, content, and element selection were identified, reflecting distinct approaches to adult learners. Program websites clustered into "sage on the stage" and "guide on the side" formats. Few websites incorporated features caregivers previously identified as useful, with only 6% including the four most helpful features.
    Conclusions: We highlight the paucity of accessible caregiver-oriented information about neurodevelopment on congenital cardiac surgical programs' websites in the United States and characterize two primary website models. The lack of information may negatively impact caregiver understanding of and engagement with neurodevelopmental services for their child with a congenital heart defect. Future research should explore the impact of each website model on caregiver understanding of and engagement with neurodevelopmental services, as well as the generalizability of these findings to other domains of pediatric subspecialty care.
    Keywords:  OHIS; cardiac surgery; caregiver; digital media; heart defects, congenital; neurodevelopment; online health information seeking; pediatric hospital; qualitative research; quality of life
    DOI:  https://doi.org/10.2196/70630
  40. JNCI Cancer Spectr. 2026 Jul 17. pii: pkag077. [Epub ahead of print]
       PURPOSE: Aboriginal and Torres Strait Islander peoples experience greater cancer incidence rates, unmet information needs, and poorer cancer outcomes compared to the general Australian population. This environmental scan evaluated cancer websites for Aboriginal and Torres Strait Islander peoples in Australia.
    METHODS: An environmental scan using eight Google searches was conducted in July 2025 to identify cancer-related websites targeted toward Aboriginal and Torres Strait Islander peoples in Australia. Patient-facing resources were assessed for readability, understandability, and actionability using grade level assessments of readability and the Patient Education Materials Assessment Tool. Cultural relevance was assessed using an Aboriginal cultural relevance measure.
    RESULTS: Of 239 search results, twelve websites were patient facing and contained material for Aboriginal and Torres Strait Islander readers, primarily on common cancers. Readability generally exceeded the recommended Grade 6 reading level, with only one resource below Grade 6 level. Resources were considered understandable with scores of > 80% for 7/12 websites (range 50-100%); 2/12 included summaries and 7/12 plain language. Actionability scores ranged from 0 to 100% (M = 63.6%, SD = 37.0), with wide variation. Cultural relevance averaged 3.8/7 (SD = 1.9) with only one resource meeting all criteria (7/7). Most lacked culturally specificity and did not provide Aboriginal and Torres Strait Islander languages translations.
    CONCLUSION: Our results necessitate co-design with Aboriginal and Torres Strait Islander peoples for resources appropriate to community needs and including information on a broad range of cancers. Websites should optimise the readability and cultural safety of information for Aboriginal and Torres Strait Islander peoples seeking cancer information.
    Keywords:  Aboriginal health; First Nations Australians; Indigenous Australians; cancer outcomes; environmental scan; health disparities; health literacy; information needs; medical information seeking; oncology information; online information
    DOI:  https://doi.org/10.1093/jncics/pkag077
  41. Gynecol Oncol. 2026 Jul 11. pii: S0090-8258(26)02055-X. [Epub ahead of print]211 142-147
       OBJECTIVE: The National Institute of Health (NIH) and American Medical Association (AMA) recommend patient education material be written at or below a 7th grade reading level. The objective of this study was to assess the reading level of gynecologic cancer patient education material published by National Cancer Institute designated cancer centers (NCI-DCCs) and patient-directed non-profit organizations (NPOs).
    METHODS: We identified online patient education material related to ovarian, uterine, cervical, vaginal, and vulvar cancer from NCI-DCCs and NPOs. We calculated the Flesch-Kincaid reading level (FKGL), Simple Measure of Gobbledygook (SMOG), and Coleman-Liau reading level using the three online calculators. Results were compared with linear mixed models.
    RESULTS: We identified 61 NCI-DCCs and 6 NPOs with patient focused information regarding cervical, ovarian, uterine, vulvar, and vaginal cancer. The mean (standard deviation [SD]) FKGL across all NCI-DCCs (n = 202) was 10.2 (1.9). Only 5.4% were at or below a 7th grade reading level for any gynecologic cancer. The FKGL from the NPOs (n = 29) was 10.0 (1.9), with 0% at or below a 7th grade reading level. The average Coleman-Liau and SMOG reading levels were also higher than a 6-7th grade reading level for both NPOs and NCI-DCCs.
    CONCLUSION: Only 5.4% of NCI-DCC's and 0% of NPO's patient education sites for gynecologic cancers are at the recommended reading level of the NIH and AMA, and the majority were at or above a high school reading level. NCI-DCCs and NPOs should improve the readability of their patient education materials to ensure all patients can understand their disease.
    Keywords:  Gynecologic oncology; Patient education; Readability
    DOI:  https://doi.org/10.1016/j.ygyno.2026.07.002
  42. BMC Oral Health. 2026 Jul 14.
       BACKGROUND: Dental implant therapy is a highly predictable and widely utilized modality for the rehabilitation of partial and complete edentulism, spanning various configurations including single crowns, fixed dental prostheses, and overdentures. However, diverse surgical, biological, and mechanical complications can occur across all rehabilitation types, potentially compromising clinical outcomes and patient satisfaction. YouTube™ is a widely used source of health-related information due to its accessibility, but the lack of peer review raises concerns regarding the accuracy and completeness of its content. Although previous studies have evaluated YouTube™ videos related to dental implants, those specifically focusing on implant-related complications remain limited. Therefore, this study aimed to critically evaluate the informativeness, quality, and demographic characteristics of YouTube™ videos related to dental implant complications and to assess the information they provide to patients and dental professionals.
    METHODS: This study evaluated the informativeness of YouTube™ videos on dental implant complications using a structured assessment framework derived from Newman and Carranza's Clinical Periodontology and Implantology. Using the keyword "dental implant complications" the first 300 videos were screened, and 139 eligible videos were included. Videos were assessed based on the presence of key complication-related topics defined in the reference textbook. In addition, video duration, number of likes, interaction rate, and audiovisual quality were analyzed.
    RESULTS: Most videos were classified as having a "moderate" level of information, with only 15.1% rated as "very good." Significant differences were observed in video duration, number of likes, interaction rates, and visual quality across different information levels. Video duration also varied significantly according to the upload source, while audiovisual quality differed based on publication year (p < 0.05).
    CONCLUSIONS: The limited informativeness of YouTube™ videos on dental implant complications may contribute to inadequate patient understanding and unrealistic expectations. These findings highlight the need to improve the quality of online patient education and emphasize the role of dental professionals in providing accurate and comprehensive digital health information.
    Keywords:  Dental implants; Health education; Health information; Social media; Surgical complications
    DOI:  https://doi.org/10.1186/s12903-026-09224-3
  43. J Pak Med Assoc. 2026 Jul;76(7): 1084-1089
       OBJECTIVE: To evaluate the educational quality of laparoscopic appendectomy videos on YouTube as a learning resource for surgeons and trainees.
    Methods: The retrospective, cross-sectional study was conducted at the Department of Paediatric Surgery, Ordu University, Ordu, Turkiye, between March and June 2024, and comprised laparoscopic appendectomy YouTube videos uploaded between January 2010 and June 2024, assessing video origin, upload date, duration, Journal of the American Medical Association score, educational quality rating score, objective component rating score, video power index, and total video quality score. Three laparoscopically experienced surgeons and three senior paediatric surgery residents independently evaluated the videos. Data was analysed using R 4.0.5.
    RESULTS: Of the 64 videos reviewed, 38(59%) originated from India. Of the 20(31.2%) videos with a video power index score >10, 15(75%) had moderate quality, 3(15%) poor and 2(10%) good. Educational quality rating score and objective component rating score showed no significant differences between the groups of senior paediatric surgery residents and laparoscopically experienced surgeons (p>0.05). However, expertise influenced total video quality score (p<0.05), with senior paediatric surgery residents and laparoscopically experienced surgeons rating 40(62%) and 27(42%) videos, respectively, as of moderate quality. Poor ratings were given to 20(31.3%) videos by senior paediatric surgery residents, and to 34(53.1%) videos by laparoscopically experienced surgeons. Good ratings were given to 4(6%) videos by senior paediatric surgery residents, and to 3(4%) videos by laparoscopically experienced surgeons.
    Conclusion: Most laparoscopic appendectomy videos on YouTube were found to be of moderate to poor quality, highlighting the need for curated content and expert guidance to enhance learning outcomes.
    Keywords:  Laparoscopy, Appendectomy, Education, YouTube, Appendicitis, Surgical education.
    DOI:  https://doi.org/10.47391/JPMA.30132
  44. J Cancer Educ. 2026 Jul 15.
      Scans constitute an important role in cancer management. "Scanxiety", or scan-associated anxiety, is commonly experienced by patients undergoing cancer-related scans. Although more patients are turning to the internet and social media platforms such as YouTube for health information, few studies have evaluated the scanxiety content presented in these online resources. This study aims to evaluate YouTube videos and webpages available to patients relating to scanxiety. YouTube was searched using pre-defined scanxiety search terms and a Google search analysis for the term scanxiety was carried out and results from the first two pages were collected in August 2024. YouTube videos and webpages were evaluated using the validated DISCERN quality criteria for consumer health information and the Agency for Healthcare Research and Quality Patient Education Materials Assessment Tool (PEMAT). To assess accessibility of reading content, the Flesch-Kincaid reading level was determined for each webpage. Three independent reviewers performed video and webpage analysis. There were 82 videos and 18 webpages included in the analysis. Videos had a mean independent-reviewer rated quality DISCERN score of 1.8 out of 5, which was between low and moderate quality. Websites showed a higher mean quality DISCERN score of 2.7 out of 5. Despite better quality of information, websites were written at a higher reading level than that of the general population, limiting accessibility. Both videos and webpages had low actionability as patient education materials. Efforts are needed to improve scanxiety-related patient education as the number of patients living with cancer and requiring cancer-related scans has significantly increased.
    Keywords:   Social media; Patient education; Scanxiety; YouTube
    DOI:  https://doi.org/10.1007/s13187-026-02950-w
  45. Hum Vaccin Immunother. 2026 Dec;22(1): 2703416
      Meningococcal infections represent a significant public health concern due to their high mortality and morbidity rates. In Turkey, meningococcal vaccines are not included in the routine immunization schedule, and families increasingly seek health-related information through digital platforms. YouTube, as a widely used video-sharing platform, has the potential to influence public attitudes toward vaccination; however, concerns remain regarding the reliability and quality of its content. This study aimed to evaluate the characteristics and quality of YouTube videos related to meningococcal vaccines in Turkey. This cross-sectional study analyzed 158 YouTube videos retrieved using the keywords "meningococcal vaccines" and "private vaccines." Videos were evaluated according to uploader type, content features, message tone, and audio-visual quality. The Global Quality Scale (GQS) and the Journal of the American Medical Association (JAMA) benchmark criteria were used for quality assessment. Of the videos, 55.7% were uploaded by physicians and 92.4% were intended for patient education. Most videos (86.7%) conveyed positive messages about vaccination. Physician-produced videos demonstrated significantly higher quality scores, while videos uploaded by pharmaceutical companies were more up-to-date and had higher view counts. Overall, a considerable proportion of the videos were of moderate or low quality. The quality and reliability of YouTube content on meningococcal vaccines vary substantially. Increasing the availability of evidence-based, high-quality content and encouraging greater involvement of healthcare professionals in digital media are essential to support informed vaccination decisions and reduce vaccine hesitancy.
    Keywords:  Meningococcal vaccine; YouTube; digital health; health information quality
    DOI:  https://doi.org/10.1080/21645515.2026.2703416
  46. BMC Oral Health. 2026 Jul 17.
       OBJECTIVE: This study aimed to evaluate the quality, reliability, and content level of maxillofacial infection-related videos on YouTube.
    MATERIALS AND METHODS: A total of 101 videos meeting the predefined inclusion criteria were analyzed. The videos were independently evaluated by two researchers in terms of content scope, quality (Global Quality Scale, GQS), reliability (modified DISCERN and JAMA criteria), and user engagement metrics.
    RESULTS: The overall quality and reliability of the videos were low to moderate. The mean GQS score was 2.96 ± 0.71, reflecting an overall moderate level of video quality. The content predominantly focused on etiology, clinical findings, and diagnosis, whereas information regarding treatment was relatively limited. As the number of views, likes, and comments increased, the quality and reliability tended to decrease; conversely, longer video duration was associated with higher quality and more comprehensive content. Videos uploaded by healthcare professionals and institutions were found to be more reliable.
    CONCLUSION: The quality of maxillofacial infection-related videos on YouTube is variable, with particularly insufficient coverage of treatment-related information. Popular videos are not necessarily reliable. Therefore, there is a clear need for more accurate and comprehensive content.
    Keywords:  DISCERN; Facial space infection; Global Quality Scale; JAMA; Maxillofacial infection; Odontogenic infection; Video analysis; YouTube
    DOI:  https://doi.org/10.1186/s12903-026-09027-6
  47. Front Oral Health. 2026 ;7 1870413
       Introduction: Short-video platforms are increasingly utilized as preliminary sources of oral health information, yet the clinical value of patient-facing content varies across platforms. This study evaluated and compared the educational usability, clinical accuracy, transparency, and misinformation risk of apical surgery videos across four major Chinese social media platforms.
    Methods: A standardized search using the term "" (apical surgery) was conducted on March 15, 2026, across TikTok, Bilibili, Kwai, and WeChat Channels. To minimize algorithmic bias, newly registered accounts under default sorting criteria were used to select 150 consecutive eligible videos per platform (N = 600). Videos were evaluated using the Patient Education Materials Assessment Tool (PEMAT), a study-specific 12-item clinical accuracy checklist, and JAMA benchmarks. Multivariable linear regression identified independent predictors of clinical accuracy.
    Results: Bilibili and WeChat Channels demonstrated significantly higher educational usability and clinical accuracy than TikTok and Kwai (P < 0.001). Median PEMAT understandability scores were 85% for Bilibili and 82% for WeChat Channels, versus 68% for TikTok and 70% for Kwai. Mean clinical accuracy scores (out of 24) were 20.1 ± 2.8 and 19.5 ± 3.0 for Bilibili and WeChat Channels, compared to 12.4 ± 3.2 for TikTok and 12.6 ± 3.4 for Kwai. Sourcing transparency was generally deficient; only 9.2% of the total sample provided references to evidence sources. Severe misinformation was significantly more prevalent on Kwai (18.7%) and TikTok (15.3%) than on Bilibili (4.0%) and WeChat Channels (5.3%) (P < 0.001). Multivariable analysis confirmed that professional uploader status (β = 3.85, P < 0.001) and longer video duration (β = 1.72 per 60 s, P < 0.001) were independent predictors of superior clinical accuracy.
    Conclusion: Patient-directed information regarding apical surgery is highly heterogeneous and strongly modulated by platform ecology. Public health initiatives should encourage qualified endodontic professionals to actively participate in video creation and advocate for platform environments that privilege balanced, source-verified clinical communication.
    Keywords:  apical surgery; clinical accuracy; misinformation; oral health communication; patient education; short-video platforms
    DOI:  https://doi.org/10.3389/froh.2026.1870413
  48. J Hum Lact. 2026 Jul 17. 8903344261452013
       BACKGROUND: Lactational mastitis is a prevalent postpartum infection that significantly impacts maternal health and the continuation of breastfeeding. While TikTok has emerged as a major platform for health information dissemination, concerns persist regarding the scientific quality and reliability of its content. To date, a systematic evaluation of lactational mastitis-related videos on this platform is lacking.
    OBJECTIVE: To evaluate the characteristics, content completeness, quality, and reliability of Chinese TikTok videos on lactational mastitis.
    METHODS: A cross-sectional content analysis was conducted on videos related to lactational mastitis on TikTok (Douyin). Between October 26 and 30, 2025, the top 150 videos returned under the default ranking were consecutively screened, of which 124 met the eligibility criteria and were included in the analysis. We used the Global Quality Score (GQS) to evaluate overall educational value, flow, and usefulness, and the modified DISCERN (mDISCERN) to assess information reliability and traceability; both are widely used and validated instruments for assessing online health information. Data were analyzed using descriptive statistics, group comparisons (Kruskal-Wallis), and Spearman correlations (two-sided p < 0.05) in R 4.3.2.
    RESULTS: Of 150 consecutively retrieved videos, 124 met the eligibility criteria. Videos were brief (median 68.00 s; IQR 42.00-111.75) and highly engaging, but overall educational quality and reliability were low, with median GQS and mDISCERN scores of 2.00 (IQR 2.00-3.00) and 3.00 (IQR 2.00-4.00), respectively. Content emphasized precipitating factors, clinical presentation, and treatment, whereas epidemiology and diagnosis were least covered; 83.9% of videos mentioned neither. Healthcare professionals scored higher than nonprofessionals on both GQS and mDISCERN (both p < 0.05). Video duration was weakly positively correlated with GQS (r = 0.35, p < 0.001), whereas engagement metrics were not meaningfully associated with GQS or mDISCERN (all ∣r∣ ≤ 0.18, p ≥ 0.05).
    CONCLUSION: The overall quality and reliability of TikTok videos on lactational mastitis are suboptimal. Future efforts should prioritize improving content completeness and scientific rigor; fostering greater participation by qualified health professionals; and promoting transparent citation of sources, clear disclosure of credentials and conflicts of interest, and routine updates to reflect current evidence. Educational interventions and authoring guidance tailored to short-video formats may further support accuracy and clarity for lay audiences.
    Keywords:  TikTok; breastfeeding; health communication; information quality; lactation; lactational mastitis; social media; 健康信息; 可靠性; 哺乳期乳腺炎; 抖音; 短视频; 质量评价
    DOI:  https://doi.org/10.1177/08903344261452013
  49. J Cardiothorac Surg. 2026 Jul 15.
       BACKGROUND: The rapid growth of short-form social media and video-sharing platforms has greatly expanded public access to health information; however, the characteristics and quality of videos concerning ventricular septal defect (VSD) have not yet been systematically evaluated. Although VSD is a well-established congenital heart disease, patients and caregivers may still rely on social media to understand diagnosis, prognosis, follow-up, and treatment options. In this study, the intended audience was defined as patient-facing general users, particularly patients, parents, and caregivers who may have encountered the clinical diagnosis term "ventricular septal defect" in medical consultations, ultrasound reports, or discharge summaries and subsequently searched for related explanations online. Therefore, evaluating the quality of highly visible VSD-related videos is relevant to patient education and digital health communication. This study aimed to assess the quality and reliability of short-form videos related to VSD on the Douyin (Chinese TikTok) and Bilibili platforms.
    METHODS: The Chinese keyword "" was used to retrieve relevant videos from Douyin and Bilibili. This clinical term was selected because the study focused on users who search after exposure to a formal diagnosis; colloquial expressions such as "heart hole" were not included because they may retrieve heterogeneous videos on congenital heart disease in general rather than VSD-specific content. This choice is acknowledged as a limitation.To capture the content most likely to be encountered by general users, the first 100 videos returned by each platform's default search order were screened, yielding 129 videos for final analysis. The Global Quality Score was defined as the primary outcome because it provides an overall assessment of educational quality and usefulness for patients. The Video Information and Quality Index, Patient Education Materials Assessment Tool, JAMA Benchmark criteria, and modified DISCERN were used as secondary complementary measures assessing audiovisual presentation, understandability/actionability, transparency, and reliability, respectively.Video eligibility was assessed independently by two reviewers, and disagreements were resolved through discussion with a third reviewer. Because reviewer-level screening decisions were not retained, Cohen's κ for eligibility screening could not be calculated.
    RESULTS: Within the sampled top-ranked videos, Douyin videos demonstrated significantly higher overall quality than those on Bilibili, particularly with respect to mDISCERN, GQS, and VIQI scores. Spearman correlation analysis revealed no significant association between video quality and audience engagement on Bilibili (p > 0.05). In contrast, on Douyin, PEMAT and VIQI scores showed moderate positive correlations with likes, collections, and shares (r = 0.48-0.64, p < 0.001). Across the whole sample, engagement indicators such as likes, collections, comments, and shares were highly correlated with one another, suggesting that these variables mainly reflected a shared popularity construct rather than independent dimensions of educational quality. However, these findings should be interpreted as platform- and sample-specific observations rather than evidence of inherent superiority of one platform over another.
    CONCLUSIONS: Among the top-ranked Chinese-language VSD-related videos included in this cross-sectional analysis, Douyin showed higher scores on several quality and reliability measures than Bilibili. Nevertheless, the overall quality of videos on both platforms remained low to moderate, and high-quality, transparent, evidence-based videos were not consistently available. These findings suggest that clinicians, educators, professional organizations, and platforms should collaborate to improve the reliability, transparency, and patient-centeredness of short-form VSD educational videos.
    Keywords:  Bilibili; Douyin; Health information; Social media; Ventricular septal defect; Video quality
    DOI:  https://doi.org/10.1186/s13019-026-04509-8
  50. BMC Gastroenterol. 2026 Jul 13.
       BACKGROUND: Gastrointestinal endoscopy is essential for diagnosing digestive diseases, yet public misconceptions persist. Short-video platforms (TikTok, Bilibili, Kwai) are now major health-information sources, but the quality of endoscopy-related content is inconsistent.
    METHODS: We analyzed 300 endoscopy videos from the three platforms, categorizing uploaders as medical or non-medical professionals and scoring content with Global Quality Score (GQS), Journal of the American Medical Association (JAMA) benchmark criteria and Modified DISCERN scores across six dimensions (definition, eligibility, preparation, procedure, techniques, management).
    RESULTS: Of the 300 videos analyzed, 63.3% were uploaded by medical professionals, with the highest proportion on TikTok (91.0%). Non-medical professionals contributed 36.7% of the videos, primarily on Bilibili and Kwai. TikTok and Kwai videos had higher user engagement (likes, comments, collections) but were shorter in duration compared to Bilibili videos. Quality assessments revealed significant differences among platforms, with TikTok videos scoring higher in reliability and accuracy (JAMA and Modified DISCERN scores) than Bilibili and Kwai videos. However, content completeness was generally low across all platforms, with most videos lacking comprehensive information on key aspects of gastrointestinal endoscopy.
    CONCLUSIONS: The quality and comprehensiveness of gastrointestinal endoscopy-related videos on TikTok, Bilibili, and Kwai are unsatisfactory, with significant variations in content quality and user engagement. Efforts should be intensified to improve video content quality, encourage more contributions from medical professionals, optimize algorithms to prioritize high-quality content, and implement stricter content reviews to enhance public health education and the dissemination of accurate medical information.
    Keywords:  Bilibili; Gastrointestinal endoscopy; Health information; Kwai; Social media platforms; TikTok; Video quality
    DOI:  https://doi.org/10.1186/s12876-026-05108-6
  51. BMC Oral Health. 2026 Jul 13.
       BACKGROUND: Pulpitis is one of the major causes of acute orofacial pain globally, yet public understanding of its early signs and the importance of timely intervention remains insufficient. Despite the growing use of social media platforms for sharing health-related information, no systematic evaluation has been conducted on the quality of pulpitis-related content available on these platforms. This study aims to assess the completeness, quality, reliability, understandability and actionability of pulpitis-related videos on Douyin, Xiaohongshu, and Bilibili, with particular attention to the role of dental practitioners in shaping video quality.
    METHODS: Three hundred twenty-nine videos from Douyin, Xiaohongshu and Bilibili were included in the analysis. For each video, data were collected on duration, likes, comments, collections, number of days online, uploader's follower count and video source. Content completeness was assessed using a self-developed checklist, while video quality was evaluated using the Global Quality Score (GQS), modified DISCERN (mDISCERN) and the Patient Education Materials Assessment Tool (PEMAT). Statistical analysis was performed using non-parametric tests, the Fisher's exact test and Spearman's correlation.
    RESULTS: Among the 329 videos analyzed, Xiaohongshu consistently outperformed Bilibili across all evaluated metrics (p < 0.05), and surpassed Douyin in all measures except understandability (p < 0.05 except PEMAT-U). Bilibili had the longest median video duration (145 s) but received the lowest quality scores (p < 0.05 except mDISCERN compared with Douyin), whereas Douyin demonstrated the highest level of user engagement (Likes, Comments, Collections, p < 0.01) alongside the shortest median duration (40 s). Videos uploaded by dental practitioners achieved significantly higher reliability scores across all three platforms (p < 0.05). Within the practitioner-generated subgroup, Xiaohongshu ranked highest in content completeness, quality, reliability and actionability (p < 0.05). On Douyin, video duration correlated positively with all five quality metrics (ρ = 0.222-0.468, p < 0.05); on Xiaohongshu, significant positive correlations were found for four metrics (ρ = 0.204-0.388, p < 0.05).
    CONCLUSIONS: Among the three platforms examined, Xiaohongshu demonstrated higher quality and content scores in the sampled recommended videos, suggesting it may be a more suitable source for pulpitis-related health information. Dental practitioners were found to consistently enhance video quality and reliability, and video duration was positively associated with content quality. These findings underscore the potential value of developing educational videos that are not only comprehensive but also understandable and actionable. Greater engagement by dental practitioners in creating such content may facilitate more effective dissemination of health information and could support public education on pulpitis.
    Keywords:  Bilibili; Douyin; Pulpitis; Quality analysis; Social media; Xiaohongshu
    DOI:  https://doi.org/10.1186/s12903-026-09136-2
  52. J Thorac Dis. 2026 Jun 30. 18(6): 600
       Background: Lung cancer remains a major public health burden, and short-video platforms have become an important source of health information. This study aimed to evaluate the characteristics and information quality of lung cancer-related videos on TikTok, Xiaohongshu, and Bilibili.
    Methods: A cross-sectional observational study was conducted using lung cancer-related videos collected on December 25, 2025. "Lung cancer" was used as the search keyword on TikTok, Xiaohongshu, and Bilibili. After duplicates and irrelevant content were removed, 563 videos were analyzed. Video characteristics, uploader types, and content categories were recorded. Information quality and reliability were evaluated using the Global Quality Scale (GQS) and the Decision-making Information Support Criteria for Evaluating the Reliability of Non-randomized Studies (DISCERN) instrument by two independent reviewers. Multivariate ordinal logistic regression was used to examine the associations among uploader type, interaction metrics, video content, and video presentation format with video quality (GQS) and information reliability (DISCERN) separately.
    Results: Bilibili videos exhibited significantly higher user engagement metrics than TikTok and Xiaohongshu videos (all P<0.001). Regarding information quality, TikTok videos achieved significantly higher GQS scores than Xiaohongshu videos and higher DISCERN scores than both Xiaohongshu and Bilibili videos. Content produced by professional institutions and individuals scored significantly higher than that from non-professional sources. Videos focusing on lung cancer classification and disease mechanisms received the highest quality scores. A strong positive correlation was observed among popularity metrics. However, no significant correlation was observed between popularity indicators and quality scores across all three platforms. Ordinal logistic regression revealed that professional institution uploaders [odds ratio (OR) =5.33, P=0.03] and lung cancer classification content (OR =2.09, P=0.01) were independently associated with higher GQS scores. For DISCERN scores, professional individual uploaders (OR =8.81, P<0.001) and lecture-style presentations (OR =8.82, P=0.02) were significantly associated with higher scores.
    Conclusions: Lung cancer-related short videos on Chinese platforms demonstrate high user engagement but considerable variability in information quality across platforms. Enhanced professional involvement, improved platform governance, and increased public critical literacy are urgently needed to optimize digital health communication for lung cancer prevention and control.
    Keywords:  Lung cancer; health information quality; medical science; short video platform
    DOI:  https://doi.org/10.21037/jtd-2026-0810
  53. J Med Internet Res. 2026 Jul 15. 28 e93578
       Background: Online health information-seeking (OHIS) behavior shapes health self-management, and eHealth literacy-the ability to seek, appraise, and apply electronic health information-is regarded as its key driver. Previous reviews aggregated heterogeneous outcomes, focused on measurement properties, or examined single clinical populations, without isolating the eHealth literacy-OHIS link.
    Objective: This study quantified the strength and heterogeneity of the eHealth literacy-OHIS association and identified its boundary conditions across generation, morbidity status, and information source credibility.
    Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), we searched PubMed, Embase, Web of Science Core Collection, PsycINFO, Psychology and Behavioral Sciences Collection, and Library, Information Science, and Technology Abstracts (LISTA) up to March 15, 2026 (PROSPERO [International Prospective Register of Systematic Reviews] CRD420251088300). Eligible studies enrolled participants, measured eHealth literacy with validated instruments, and assessed OHIS. Risk of bias used the modified Newcastle-Ottawa Scale. Correlations were Fisher z-transformed and pooled under a random-effects model with the Hartung-Knapp-Sidik-Jonkman correction; subgroups were age cohort, morbidity status, and source type. Heterogeneity was quantified with I² and τ²; a univariate meta-regression examined temporal trends, and certainty of evidence was rated using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation).
    Results: Of 9249 nonduplicate records, 32 studies entered the qualitative synthesis, and 19 (20 effect sizes) the meta-analysis. The grand mean correlation was 0.27 (95% CI 0.15-0.38; P<.001) but is of limited interpretive value given extreme heterogeneity (I²=99%; τ²=0.064; 95% prediction interval -0.26 to 0.67). Correlations were stronger in non-Gen Z (k=12; r=0.39; 95% CI 0.27-0.50; P<.001) than in Gen Z (k=8; r=0.07; 95% CI -0.06 to 0.20; P=.23), in patients (k=3; r=0.58; 95% CI 0.01-0.86; P=.049) than in nonpatients (k=17; r=0.22; 95% CI 0.11-0.32; P<.001), and in professional (k=5; r=0.41; 95% CI 0.11-0.64; P=.02) than in nonprofessional (k=14; r=0.21; 95% CI 0.06-0.35; P=.01) sources. Meta-regression on collection year showed no significant temporal change (b=-0.005 per year; P=.55), and neither the Egger test (P=.60) nor trim-and-fill indicated small-study effects.
    Conclusions: The eHealth literacy-OHIS association is best understood through its boundary conditions, not the overall estimate. The association was robust in non-Gen Z and professional-source contexts but near-null in Gen Z, showing that the eHealth literacy scale's behavioral predictive validity is cohort- and platform-dependent. Interventions for Gen Z and nonpatient populations should pair literacy training with motivational cues and professionally curated information environments. GRADE certainty was very low, underscoring the need for longitudinal, performance-based research.
    Keywords:  Generation Z; digital health literacy; eHealth literacy; generational differences; meta-analysis; online health information seeking behavior
    DOI:  https://doi.org/10.2196/93578
  54. Environ Health Prev Med. 2026 ;31 48
       BACKGROUND: Accessible and trustworthy health information sources support informed decisions. Longitudinal evidence on how health information sources influence behavioural changes in community-dwelling populations remains limited. Thus, we examined associations between health information sources and behaviour changes in walking time, balanced meal consumption, and smoking cessation.
    METHODS: Baseline (2021) and follow-up (2023) survey questionnaires were distributed to residents randomly selected from the 18 residential areas. Health information sources were classified into five categories: print media, television (TV) and radio, the Internet, family and friends, and professionals. Desirable health behaviours were defined as walking for ≥60 min/day; consuming ≥2 meals per day comprising staple grains, main dishes, and side items (SMS); and smoking cessation. Meeting these criteria was classified as good; not meeting them as poor. Improvement was defined as poor at baseline and good at follow-up. Maintenance was defined as good at both surveys. Improvement analyses included participants with poor baseline conditions. Maintenance analyses included those with good baseline conditions. Associations were analysed using multilevel modified Poisson regression models, accounting for clustering within residential areas and adjusting for covariates. Risk ratios (RRs) and 95% confidence intervals (95% CIs) were calculated.
    RESULTS: Of the 12 119 baseline participants, 6620 (54.6%) completed the follow-up. The most common health information sources were TV and radio (68.5%), the Internet (56.8%), and print media (48.9%). Use of family and friends as information sources was associated with improvement in walking time (RR: 1.19; 95% CI: 1.02-1.39), as was use of professionals (RR: 1.15; 95% CI: 1.03-1.28). Professionals showed a marginal association with sustained walking time (RR: 1.07; 95% CI: 0.99-1.15). TV and radio was associated with improvement in SMS meal consumption (RR: 1.13; 95% CI: 1.02-1.26), while print media was associated with maintenance of SMS meal consumption (RR: 1.05; 95% CI: 1.01-1.08). No sources were related to smoking cessation.
    CONCLUSIONS: Health information sources showed distinct associations with improved and maintained health behaviours. Interpersonal sources were relevant for improving walking time, while mass media were linked to dietary improvement and maintenance. These findings suggest tailoring health communication strategies to specific health behaviours.
    Keywords:  Balanced meal consumption; Behaviour change; Health information sources; Smoking cessation; Walking time
    DOI:  https://doi.org/10.1265/ehpm.26-00060
  55. Front Public Health. 2026 ;14 1868556
       Introduction: At least 40% of cancer cases could be prevented by lifestyle changes. However, despite clear recommendations, public awareness of modifiable cancer risk factors remains limited and misconceptions about cancer causes further impede prevention efforts. Understanding knowledge of preventable factors, myths, and information sources is essential for effective cancer prevention.
    Methods: To address this, a total of 1,232 residents of Stuttgart, Germany (53.2% female; Mage  = 41.1; SD = 14.3) participated in the CLARO study (January-May 2025). A cross-sectional online survey assessed participants' knowledge of cancer risk factors and myths, cancer information overload, and sources of prevention information.
    Results: Smoking (98.5%) and sunburn (95.1%) were widely recognized as cancer risk factors, whereas high salt intake (28.1%) and prolonged sitting (29.2%) were less acknowledged. Most cancer myths went unrecognized, except physical trauma (76.9%). Higher levels of cancer risk factor knowledge were linked to higher education (middle vs. high: β = -0.069, p = 0.022; low vs. high: β = -0.053, p = 0.048), prior cancer prevention information retrieval (β = 0.186, p < 0.001), and less cancer information overload (β = -0.235, p < 0.001).
    Discussion: Persistent knowledge gaps and misconceptions highlight the need to promote access to trusted, expert-reviewed information sources.
    Keywords:  cancer awareness; cancer myths; cancer prevention; information overload; information seeking
    DOI:  https://doi.org/10.3389/fpubh.2026.1868556
  56. Arch Esp Urol. 2026 Jun;79(5): 823-830
       BACKGROUND: This study aimed to comparatively examine digital search behaviors related to urological symptoms and conditions in Türkiye, the United States, Germany, the United Kingdom, India, and Japan using Google Trends data.
    MATERIALS AND METHODS: This cross-sectional, observational digital epidemiology study analyzed weekly web search data from the past five years obtained via the Google Trends platform. Ten urological conditions were evaluated: kidney stones, benign prostatic hyperplasia, dysuria, urinary incontinence, erectile dysfunction, premature ejaculation, varicocele, testicular pain, infertility, and penile curvature. Mean Google Trends scores were used for cross-country comparisons, while country-specific digital search profiles were normalized to reflect relative distributions. Searches related to kidney stones were additionally analyzed for seasonal variation. K-means clustering analysis was applied to identify similarities in digital search profiles across countries.
    RESULTS: Kidney stones were identified as the urological condition with the highest digital search interest in all countries, with increased search activity observed during the summer months. Benign prostatic hyperplasia and dysuria were among the most frequently searched conditions across all countries. Searches related to reproductive and sexual health showed considerable variation between countries. Clustering analysis revealed that the United States, Germany, and the United Kingdom shared similar digital search profiles, whereas India and Japan formed a distinct cluster. Türkiye demonstrated a profile broadly similar to Western countries but with partial divergence.
    CONCLUSIONS: Digital search behaviors related to urological symptoms and conditions exhibit heterogeneous patterns across countries. Google Trends data may serve as a complementary tool for assessing population-level urological health awareness.
    Keywords:  Google Trends; digital epidemiology; health information seeking behavior; kidney calculi; urology
    DOI:  https://doi.org/10.56434/j.arch.esp.urol.20267905.96