ArticleJAMIA open2025
A proof-of-concept study for patient use of open notes with large language models.
Article in JAMIA open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed.
- AI and Patient Trust in Health Care.Journal of medical Internet research · 2026Article
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Article
- Article
- Errors in AI-Transformed Patient-Centered Mental Health Documentation Written by Psychiatrists: Qualitative Pre-Post Study.JMIR mental health · 2026Article
- Open Notes in Mental Health: A Scoping Review of Stakeholder Experiences and Implications for Clinical Practice.Healthcare (Basel, Switzerland) · 2025Review
- Artificial Intelligence Versus Professional Standards: A Cross-Sectional Comparative Study of GPT, Gemini, and ENT UK in Delivering Patient Information on ENT Conditions.Diseases (Basel, Switzerland) · 2025Article
- 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 · 2025Article
- Health Consumers' Use and Perceptions of Health Information from Generative Artificial Intelligence Chatbots: A Scoping Review.Applied clinical informatics · 2025Article
- Evaluating performance of large language models for atrial fibrillation management using different prompting strategies and languages.Scientific reports · 2025Article
- The Impact of Google Search versus ChatGPT on Patient Understanding and Potential Adherence in PICC Line Care: A Comparative Analysis.Patient preference and adherence · 2025Article
- Critical AI Health Literacy as Liberation Technology: A New Skill for Patient Empowerment.NAM perspectives · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The use of large language models (LLMs) is growing for both clinicians and patients. While researchers and clinicians have explored LLMs to manage patient portal messages and reduce burnout, there is less documentation about how patients use these tools to understand clinical notes and inform decision-making. This proof-of-concept study examined the reliability and accuracy of LLMs in responding to patient queries based on an open visit note. Materials and Methods: In a cross-sectional proof-of-concept study, 3 commercially available LLMs (ChatGPT 4o, Claude 3 Opus, Gemini 1.5) were evaluated using 4 distinct prompt series- Results: Overall, the Standard and Persona-based prompt series yielded the best results across all criterion regardless of LLM. Chat-GPT 4o using Persona-based prompts scored highest in all categories. All LLMs scored low in the use of Discussion: This proof-of-concept study highlighted the potential for LLMs to assist patients in interpreting open notes. The most effective LLM responses were achieved by applying Conclusion: Optimizing LLMs for patient-driven queries, and patient education and counseling around the use of LLMs, have potential to enhance patient use and understanding of their health information.
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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.