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This paper is in the following e-collection/theme issue:

Generative Language Models Including ChatGPT (890) Demographics of Users, Social & Digital Divide (995) eHealth Literacy / Digital Literacy (593) Information Seeking, Information Needs (601) Chatbots and Conversational Agents (809) Assessing and Building eHealth / Digital Literacy in Populations (72)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55138, first published .
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Characterizing the Adoption and Experiences of Users of Artificial Intelligence–Generated Health Information in the United States: Cross-Sectional Questionnaire Study

Characterizing the Adoption and Experiences of Users of Artificial Intelligence–Generated Health Information in the United States: Cross-Sectional Questionnaire Study

Authors of this article:

Oluwatobiloba Ayo-Ajibola1 Author Orcid Image ;   Ryan J Davis1 Author Orcid Image ;   Matthew E Lin2 Author Orcid Image ;   Jeffrey Riddell3 Author Orcid Image ;   Richard L Kravitz4 Author Orcid Image
Article Authors Cited by (37) Tweetations Metrics

Journals

  1. Ayo-Ajibola O, Julien C, Lin M, Riddell J, Duan N, Kravitz R. Association of Primary Care Access with Health-Related ChatGPT Use: A National Cross-Sectional Survey. Journal of General Internal Medicine 2026;41(2):338 View
  2. Liu J, Segal K, Daher M, Ozolin J, Binder W, Bergen M, McDonald C, Owens B, Antoci V. Artificial intelligence versus orthopedic surgeons as an orthopedic consultant in the emergency department. Injury 2025;56(4):112297 View
  3. Young A, Omosun F. A comparative analysis of CDC and AI-generated health information using computer-aided text analysis. Journal of Communication in Healthcare 2025;18(3):205 View
  4. Thompson P, Thornton R, Ramsden C. Assessing chatbots ability to produce leaflets on cataract surgery: Bing AI, chatGPT 3.5, chatGPT 4o, ChatSonic, Google Bard, Perplexity, and Pi. Journal of Cataract & Refractive Surgery 2025;51(5):371 View
  5. Alain G, Crick J, Snead E, Quatman-Yates C, Quatman C. Evaluating User Interactions and Adoption Patterns of Generative AI in Health Care Occupations Using Claude: Cross-Sectional Study. Journal of Medical Internet Research 2025;27:e73918 View
  6. Lim J, Hong N. Perceived Search Overload, Generative AI Credibility, and Comparative Usefulness: A Channel Complementarity Approach to Health Information Seeking. Health & New Media Research 2025;9(1):124 View
  7. Bautista J, Herbert D, Farmer M, De Torres R, Soriano G, Ronquillo C. Health Consumers' Use and Perceptions of Health Information from Generative Artificial Intelligence Chatbots: A Scoping Review. Applied Clinical Informatics 2025;16(04):892 View
  8. Singla R, Lodhi S, Kibret T, Jegatheswaran J, Glavinovic T, Massicotte‐Azarniouch D, Karpinski J, Powell R, Burns K, Sood M, Bugeja A. Accuracy, Clarity, and Comprehensiveness of ChatGPT Outputs for Commonly Asked Questions About Living Kidney Donation. Clinical Transplantation 2025;39(9) View
  9. Wardle C, Urbani S, Wang E. Evolving Health Information–Seeking Behavior in the Context of Google AI Overviews, ChatGPT, and Alexa: Interview Study Using the Think-Aloud Protocol. Journal of Medical Internet Research 2025;27:e79961 View
  10. Wang A, He D, Luo Z, Ma F. User Studies in Generative Interactive IR (GenIIR): An ISIC‐Informed Systematic Review. Proceedings of the Association for Information Science and Technology 2025;62(1):1705 View
  11. Erden Y, Temel M, Bağcıer F. Evaluating ChatGPT-4 for rheumatology patient education: a comparative analysis of readability, reliability, and similarity to the American College of Rheumatology’s fact sheets. Rheumatology 2025;63(5):313 View
  12. Haber T, Hinman R, Merolli M. Uses of Artificial Intelligence in Nonpharmacological Rheumatology Care: What We Know, What We Need to Learn, and What Its Limitations Are. Arthritis Care & Research 2026;78(2):177 View
  13. Joseph S, Bhardwaj A, Skariah J, Aggarwal I, Shah V, Harris R. Effects of education level on natural language processing in cardiovascular health communication. Frontiers in Public Health 2025;13 View
  14. Liu K, Cheng L, Wang W, Wu R, Li C, Yao K, Ge J. Cross-sectional comparative evaluation of US and China-developed large language models for bilingual coronary heart disease patient education. Intelligent Medicine 2026;6(2):132 View
  15. Valcheff K, Pennington D, Keaton S, Carter K. Integrating the Essentials Core Competencies Related to Health Literacy Into Undergraduate Curriculum: Tapping Traditional and Emerging Education Strategies. Public Health Nursing 2026;43(2):402 View
  16. Shang Y, Huang X, Song K. Machine Learning and Large Language Models in Preoperative Bariatric Surgery: From Risk Assessment to Shared Decision-Making. AI Med 2025;1(3):271 View
  17. Gao Q, Chen L, Huang Z. Opportunities and challenges of artificial intelligence in public health: a systematic review on technological efficacy, ethical dilemmas, and governance pathways. Frontiers in Public Health 2026;13 View
  18. Reis F, Agha-Mir-Salim L, Hickstein R, Reis M, Piper S, Balzer F, Boie S. Disclaimers and Referral Patterns for Medical Advice Across Urgency Levels: Large Language Model Evaluation Study. Journal of Medical Internet Research 2026;28:e84668 View
  19. Uscher-Pines L, Sousa J, Raja P, Ayer L, Mehrotra A, Huskamp H, Busch A. Assessing Generative AI Chatbots for Alcohol Misuse Support: A Longitudinal Simulation Study. NEJM AI 2026;3(2) View
  20. Paik J, Choung H, Yang Q. Why People Turn to ChatGPT for Health Information: Extending UTAUT with Healthcare Dissatisfaction and Perceived Credibility. Health Communication 2026:1 View
  21. Kopka M, He L, Feufel M. Evaluating the accuracy of ChatGPT model versions for giving care-seeking advice. Communications Medicine 2026;6(1) View
  22. Kopka M, Feufel M. Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation Study. JMIR Biomedical Engineering 2026;11:e88053 View
  23. Liu D, Yuan Y. Digital divides in verification: investigating the heterogeneity of verification patterns for AI-generated health information. Asian Journal of Communication 2026:1 View
  24. Nazir T. ChatGPT for Mental Health Support: A Systematic Scoping Review of Human–Computer Interaction Implications. Health Education & Behavior 2026 View
  25. Motevalli M, Boaventura B, Stanford F. ChatGPT for obesity management: a review of evidence, potential challenges, and clinical implications. The Lancet Digital Health 2026:100980 View
  26. Wang X, Yin C, He H, Guo J, Fu X, Bai F. Benchmarking public large language model responses to patient-facing inflammatory bowel disease questions: informational quality, transparency proxies, and readability. Frontiers in Public Health 2026;14 View
  27. Al Suwaidan H, Althumairi A, Al-Rayes S, Alkhurayji K. Comparing Perceptions of ChatGPT Use in Health Attitude Contexts Among Users and Nonusers: Cross-Sectional Study. JMIR Formative Research 2026;10:e79276 View
  28. Zuo Y, Wan Q, Wang S. Patient Cognitive Bias in Large Language Model–Supported Health Consultations: A Simulation-Based Comparative Study (Preprint). Journal of Medical Internet Research 2025 View
  29. Wang J, Zhan Y, Shen Y, Cao F, Ling J. A centralized decision-making support consultation response for fertility preservation in breast cancer patients: benchmark performance of generative large language models in terms of reliability and readability. Frontiers in Public Health 2026;14 View
  30. Alon L, Levkovich I. Consumer and Patient Health Information Seeking with Generative AI Tools: A Scoping Review of Facilitators and Barriers (Preprint). Journal of Medical Internet Research 2026 View
  31. Huang R, Cecil J, Freedman M, Chattopadhyay S. Quantifying Factors that Drive Trust and Satisfaction with AI Health Chatbots: A Mixed-Methods Vignette Survey of Caregivers for Pediatric Infectious Diseases (Preprint). Journal of Medical Internet Research 2025 View
  32. Mason A, Ashmore A. Generative-AI as a source of caregiving guidance for medical tourists: A content and readability analysis. Patient Education and Counseling 2026;149:109630 View
  33. Özbakkaloğlu A, Rahman Ö, Keleş E, Daylan A, Cansu D, Bozok Ş. The Quality of AI-Generated CABG Counseling: A Blinded Comparison of Two Language Models. Journal of Clinical Medicine 2026;15(10):3896 View
  34. Mononen N, Salo M, Timonen D, Pohjanoksa-Mäntylä M, Airaksinen M. Receipt of Medicines Information From the Internet and Other Information Sources Among Adult Medicine Users in Developed Economies, 2010-2025: Systematic Review. Journal of Medical Internet Research 2026;28:e71984 View

Books/Policy Documents

  1. Lin S. Navigating Health Information in the Age of Artificial Intelligence. View
  2. Roshan R, Swetha K, Vimala M. Proceedings of the NIELIT’s International Conference on Communication, Electronics and Digital Technologies. View

Conference Proceedings

  1. Kaleva I, Zhan X, Abu-Salma R, Such J. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. Privacy and Safety Experiences and Concerns of US Women Using Generative AI for Seeking Sexual and Reproductive Health Information View