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

Chatbots and Conversational Agents (803) Participatory Medicine & E-Patients (904) Demographics of Users, Social & Digital Divide (987) eHealth Literacy / Digital Literacy (586) Information Retrieval (366) Information Seeking, Information Needs (596) Artificial Intelligence (2428) Generative Language Models Including ChatGPT (868)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64290, first published .
Laypeople’s Use of and Attitudes Toward Large Language Models and Search Engines for Health Queries: Survey Study

Laypeople’s Use of and Attitudes Toward Large Language Models and Search Engines for Health Queries: Survey Study

Laypeople’s Use of and Attitudes Toward Large Language Models and Search Engines for Health Queries: Survey Study

Authors of this article:

Tamir Mendel1 Author Orcid Image ;   Nina Singh2 Author Orcid Image ;   Devin M Mann3 Author Orcid Image ;   Batia Wiesenfeld4 Author Orcid Image ;   Oded Nov1 Author Orcid Image
Article Authors Cited by (34) Tweetations (6) Metrics Author Video

Journals

  1. Hassan M, Abdelaziz A, Abdelrahman H, Mohamed M, Ellabban M. Performance of AI‐Chatbots to Common Temporomandibular Joint Disorders (TMDs) Patient Queries: Accuracy, Completeness, Reliability and Readability. Orthodontics & Craniofacial Research 2025;28(S1) View
  2. Rebitschek F, Carella A, Kohlrausch-Pazin S, Zitzmann M, Steckelberg A, Wilhelm C. Evaluating evidence-based health information from generative AI using a cross-sectional study with laypeople seeking screening information. npj Digital Medicine 2025;8(1) View
  3. 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
  4. Ralla B, Biernath N, Lichy I, Kurz L, Friedersdorff F, Schlomm T, Schmidt J, Plage H, Jeutner J. How Accurate Is AI? A Critical Evaluation of Commonly Used Large Language Models in Responding to Patient Concerns About Incidental Kidney Tumors. Journal of Clinical Medicine 2025;14(16):5697 View
  5. 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
  6. Lee T, Nambiar K, Wong H, Leshed G. Supporting from afar: Exploring Practices and Challenges of Remote Support-Giving for Mental Health. Proceedings of the ACM on Human-Computer Interaction 2025;9(7):1 View
  7. Riek N, Gokhale T, Akcakaya M, Al-Zaiti S. Using large language models for ECG rhythm interpretation: Pitfalls, limitations, and future opportunities. Heart & Lung 2026;75:372 View
  8. Tan S, Sng G, Lee P. Accuracy of Large Language Model Responses Versus Internet Searches for Common Questions About Glucagon-Like Peptide-1 Receptor Agonist Therapy: Exploratory Simulation Study. JMIR Formative Research 2025;9:e78289 View
  9. Schuss P, Gonschorek A, Kämper M, Lemcke J, Meisel H, Rogge W, Schaan M, Schwenkreis P, Strowitzki M, Wohlfahrt K, Schmehl I. Artificial Intelligence Chatbot Responses to Patient Queries on Traumatic Brain Injury: An Expert Assessment of Reliability and Accuracy. Journal of Neurotrauma 2025 View
  10. You Q, Luo Y, Chen Z, Song X, Wang F, Wen S, Ding Q, Chen J. How large language model responds to common depression questions: A comparative analysis of ChatGPT-4.0, DeepSeek, Google Gemini and Perplexity. Nurse Education in Practice 2026;90:104668 View
  11. Alcalay I, Weissman A, Ganer Herman H, Tsafrir A, Friedman M, Weiner E, Orvieto R, Polyzos N, Dahan M, Polyakov A, Fischer R, Esteves S, Ata B, Franasiak J, Mizrachi Y. Can artificial intelligence models provide reliable medical counselling to fertility patients?. Reproductive BioMedicine Online 2026;52(2):105237 View
  12. Anibal J, Bedrick S, Nguyen H, Gunkel J, Huth H, Le T, Salvi Cruz S, Hazen L, Wood B. DeepSeek for healthcare: do no harm?. AI and Ethics 2026;6(1) View
  13. Angyal V, Bertalan Á, Domján P, Feith H, Dinya E. Development of a questionnaire for assessing the use of ChatGPT in primary and secondary disease prevention. Frontiers in Public Health 2026;13 View
  14. 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
  15. Chen M, Wu Y, Ma J, Jia X, Gao C, Zhao F, Qiao Y. Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage. npj Digital Medicine 2026;9(1) View
  16. Han B, Barnes T, Reddy C, Shin A. Evaluating Large Language Model–Generated Clinical Summaries Through a Dual-Perspective Framework: Retrospective Observational Study. JMIR AI 2026;5:e85221 View
  17. del Barrio M, Laos K, Lara M, García C, Menendez H. Size doesn’t matter: Assessing the trustworthiness of large language models in medical contexts: A focus on epidural information retrieval. Artificial Intelligence in Medicine 2026;175:103379 View
  18. Tang A, Wei M, Haemel A, La C, Sirota M, Lee E. Artificial intelligence–enabled precision medicine for inflammatory skin diseases. Journal of Investigative Dermatology 2026;146(5):1195 View
  19. Trillo-Domínguez M, Martin-Neira J, Olvera-Lobo M. Dr. Google vs. Dr. ChatGPT in Online Health Self-Consultation: A Scoping Review of Accuracy, Bias, and Actionability (2023–2025). Informatics 2026;13(3):41 View
  20. Chua D, McMahon B, Kos S. Shadow AI in Consumer Health: The Case for Safe Adoption of National Health AI Assistants. Mayo Clinic Proceedings: Digital Health 2026;4(2):100352 View
  21. Søgaard A, Rajcic N, Scott A. Epistemic Drift in Mind-Model Systems. Minds and Machines 2026;36(1) View
  22. Ozdas Sevgin D, Tarihci Cakmak E, Yildirim Ogras G, Diracoglu D. Can AI chatbots guide patients and physicians about neck pain? A reliability and readability comparison of ChatGPT-4 and Gemini. Journal of Back and Musculoskeletal Rehabilitation 2026 View
  23. Zhang R, Liu M, Liong O, Liu L, Guan S, Chen K, Li Y, Han X, Mei L. AI in the Chair: A Multi‐Centre Study of Doctor AI Answering Orthodontic Patient Questions. Journal of the Royal Society of New Zealand 2026;56(2) View
  24. Ray T, Barrow T, Hamel L, Al Hallak N, Azmi A, Shields A, Kim S, Tobon M, Beal E. Evaluating the quality of online patient education materials for gastric adenocarcinoma. Frontiers in Digital Health 2026;8 View
  25. Mamun A, Barua Soumma S, Ghasemzadeh H. Trustworthy AI in digital health: a comprehensive review of robustness and explainability. Progress in Biomedical Engineering 2026;8(2):022007 View
  26. Brandtzaeg P, Skjuve M, Følstad A. AI aversion? Effects of author disclosure on young people’s perceptions of mental health advice. Cyberpsychology: Journal of Psychosocial Research on Cyberspace 2026;20(2) View
  27. Mishra V, Rana S. Building smarter digital content: a CRITIC – DEMATEL framework for leveraging large language model optimization in marketing. Journal of Business and Socio-economic Development 2026:1 View
  28. Liu Y, Yu S, Jin H, Wen J, Qian A, Lee T, Ramsis M, Choi G, Qin L, Liu X, Wang E. A multi-agent framework combining large language models with medical flowcharts for self-triage. Nature Health 2026 View
  29. Linardon J, Messer M, Anderson C, Soliman O, Liu C, Firth J, Torous J. Fluency Without Fidelity: Errors in Citation-Attributed Claims in Large Language Model-Generated Literature Reviews in Mental Health. Journal of Technology in Behavioral Science 2026 View

Books/Policy Documents

  1. Berber A, Mijić J. Reconfiguring Human Autonomy. View

Conference Proceedings

  1. Ryser A, Allwein F, Schlippe T. 2025 3rd International Conference on Foundation and Large Language Models (FLLM). Calibrated Trust in Dealing with LLM Hallucinations: A Qualitative Study View
  2. Ramesh S, Daneshzand F, Rashidi B, Raj S, Subramonyam H, Rajabiyazdi F. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. Metacognitive Demands and Strategies While Using Off-The-Shelf AI Conversational Agents for Health Information Seeking View
  3. Tan F, Messerschmidt M, Yin W, Nov O. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. The Impact of Response Latency and Task Type on Human-LLM Interaction and Perception View
  4. Sayres R, Hao Y, Ward A, Wang A, Freeman B, Zhan S, Ardila D, Li J, Lee I, Iurchenko A, Kou S, Badola K, Hu J, Kumar B, Y Johnson K, Vijay S, Krogue J, Hassidim A, Matias Y, Webster D, Virmani S, Liu Y, Duong Q, Schaekermann M. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. Towards Better Health Conversations: The Benefits of Context-seeking View