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

Chatbots and Conversational Agents (740) Participatory Medicine & E-Patients (873) Demographics of Users, Social & Digital Divide (914) eHealth Literacy / Digital Literacy (528) Information Retrieval (348) Information Seeking, Information Needs (561) Generative Language Models Including ChatGPT (708) Artificial Intelligence (2129)

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 (9) 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 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