ArticleJAMA internal medicine2023
Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum.
Article in JAMA internal medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06108037 (Duke Health Listens Survey on Generative Artificial Intelligence), which is not on this map. Cited by 1,151 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Duke Health Listens Survey on Generative Artificial Intelligence (AI) for MyChart Messaging
Who cites it
1,151 citing papers in PubMed, 3 syntheses or guidelines pooled it, 2,093 citations in OpenAlex.
- Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Adaptation of Language Models for Clinical Decision-Making in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Impact of Large Language Model-Based AI Tools on Physician-Patient Communication: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Research on patient-facing chatbots based on large language models in the care of older people: a living systematic review.European geriatric medicine · 2026Pooled it
- Effectiveness of ChatGPT and DeepSeek in Urology Medical Education: Randomized Controlled Trial.Journal of medical Internet research · 2026Trial
- Trust in Generative AI for Health Information Consumption and the Effect of Learned Dependency: Randomized Controlled Experimental Study.Journal of medical Internet research · 2026Trial
- Preliminary Evaluation of a Large Language Model-Powered Chatbot for Osteoporosis Self-Management Education: Formative Randomized Controlled Trial.JMIR formative research · 2026Trial
- Effectiveness of Al-Assisted Patient Health Education Using Voice Cloning and ChatGPT: Prospective Randomized Controlled Trial.Journal of medical Internet research · 2026Trial
- Randomized Controlled Trial Comparing AI-Generated, Clinician-Edited to Human-Generated After-Visit Summaries.Mayo Clinic proceedings. Digital health · 2026Article
- Large Language Model-Generated Electronic Consultations: A Blinded Comparative Study of OpenEvidence and Gastroenterologist Electronic Consultations.Mayo Clinic proceedings. Digital health · 2026Article
- Real-world use and evaluation of a generative AI chatbot for Parkinson's disease information: a prospective observational study.The Lancet regional health. Europe · 2026Article
- Evaluator-Masked Assessment of 4 Consumer-Facing Large Language Models on Expert Anterior Segment Ophthalmology Questions.Ophthalmology science · 2026Article
- Performance of large language models in providing patient- oriented information on gingival recession: a comparative study.The Saudi dental journal · 2026Article
- Associations between parental generative artificial intelligence health-advice use and child vaccination uptake in Japan: an exploratory cross-sectional study.World journal of pediatrics : WJP · 2026Article
- Large Language Model Chatbot Responses to Cancer Survivorship Questions in Hong Kong: Bilingual Evaluation and Prompt Optimization Study.Journal of medical Internet research · 2026Article
- Liability and Standard of Care in AI-Driven Psychiatric Practice: European Viewpoint.Journal of medical Internet research · 2026Article
- Response Quality of AI-Generated Answers to Orthodontic Patient Concerns Across Empathy, Accuracy, Comprehensiveness, Personalisation, Safety, and Patient-Centeredness: A Cross-Model, Bilingual Evaluation of Claude, GPT-4o, and Gemini.Healthcare (Basel, Switzerland) · 2026Article
- An Evidence-Based Framework for Patient-Facing Artificial Intelligence Integration in the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- Guideline-augmented prompting improves comparative preference and response consistency of large language model outputs for orthopaedic anaesthesia questions: A controlled prompting study.Journal of experimental orthopaedics · 2026Article
- A Conversational Agent for Providing Personalized Preexposure Prophylaxis (PrEP) Support: Protocol for Chatbot Implementation and Evaluation.JMIR research protocols · 2026Article
- Ten simple rules for responsible use of AI and LLMs in computational biology.PLoS computational biology · 2026Article
1,091 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 1 country.
Funding
Abstract
Importance: The rapid expansion of virtual health care has caused a surge in patient messages concomitant with more work and burnout among health care professionals. Artificial intelligence (AI) assistants could potentially aid in creating answers to patient questions by drafting responses that could be reviewed by clinicians. Objective: To evaluate the ability of an AI chatbot assistant (ChatGPT), released in November 2022, to provide quality and empathetic responses to patient questions. Design, Setting, and Participants: In this cross-sectional study, a public and nonidentifiable database of questions from a public social media forum (Reddit's r/AskDocs) was used to randomly draw 195 exchanges from October 2022 where a verified physician responded to a public question. Chatbot responses were generated by entering the original question into a fresh session (without prior questions having been asked in the session) on December 22 and 23, 2022. The original question along with anonymized and randomly ordered physician and chatbot responses were evaluated in triplicate by a team of licensed health care professionals. Evaluators chose "which response was better" and judged both "the quality of information provided" (very poor, poor, acceptable, good, or very good) and "the empathy or bedside manner provided" (not empathetic, slightly empathetic, moderately empathetic, empathetic, and very empathetic). Mean outcomes were ordered on a 1 to 5 scale and compared between chatbot and physicians. Results: Of the 195 questions and responses, evaluators preferred chatbot responses to physician responses in 78.6% (95% CI, 75.0%-81.8%) of the 585 evaluations. Mean (IQR) physician responses were significantly shorter than chatbot responses (52 [17-62] words vs 211 [168-245] words; t = 25.4; P < .001). Chatbot responses were rated of significantly higher quality than physician responses (t = 13.3; P < .001). The proportion of responses rated as good or very good quality (≥ 4), for instance, was higher for chatbot than physicians (chatbot: 78.5%, 95% CI, 72.3%-84.1%; physicians: 22.1%, 95% CI, 16.4%-28.2%;). This amounted to 3.6 times higher prevalence of good or very good quality responses for the chatbot. Chatbot responses were also rated significantly more empathetic than physician responses (t = 18.9; P < .001). The proportion of responses rated empathetic or very empathetic (≥4) was higher for chatbot than for physicians (physicians: 4.6%, 95% CI, 2.1%-7.7%; chatbot: 45.1%, 95% CI, 38.5%-51.8%; physicians: 4.6%, 95% CI, 2.1%-7.7%). This amounted to 9.8 times higher prevalence of empathetic or very empathetic responses for the chatbot. Conclusions: In this cross-sectional study, a chatbot generated quality and empathetic responses to patient questions posed in an online forum. Further exploration of this technology is warranted in clinical settings, such as using chatbot to draft responses that physicians could then edit. Randomized trials could assess further if using AI assistants might improve responses, lower clinician burnout, and improve patient outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.