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Cited by 224Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareMachine Learning in Healthcare

The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.

An evaluation framework for clinical use of large language models in patient interaction tasks

Johri, Shreya, Jeong, Jaehwan, Tran, Benjamin A., Schlessinger, Daniel I., Wongvibulsin, Shannon, Barnes, Leandra A., Zhou, Hong-Yu, Cai, Zhuo Ran, Van Allen, Eliezer M., Kim, David, Daneshjou, Roxana, Rajpurkar, Pranav·2025·Nature Medicine
DOI 10.1038/s41591-024-03328-5
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Abstract

The integration of large language models (LLMs) into clinical diagnostics has the potential to transform doctor-patient interactions. However, the readiness of these models for real-world clinical application remains inadequately tested. This paper introduces the Conversational Reasoning Assessment Framework for Testing in Medicine (CRAFT-MD) approach for evaluating clinical LLMs. Unlike traditional methods that rely on structured medical examinations, CRAFT-MD focuses on natural dialogues, using simulated artificial intelligence agents to interact with LLMs in a controlled environment. We applied CRAFT-MD to assess the diagnostic capabilities of GPT-4, GPT-3.5, Mistral and LLaMA-2-7b across 12 medical specialties. Our experiments revealed critical insights into the limitations of current LLMs in terms of clinical conversational reasoning, history-taking and diagnostic accuracy. These limitations also persisted when analyzing multimodal conversational and visual assessment capabilities of GPT-4V. We propose a comprehensive set of recommendations for future evaluations of clinical LLMs based on our empirical findings. These recommendations emphasize realistic doctor-patient conversations, comprehensive history-taking, open-ended questioning and using a combination of automated and expert evaluations. The introduction of CRAFT-MD marks an advancement in testing of clinical LLMs, aiming to ensure that these models augment medical practice effectively and ethically.

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Crossref · 10.1038/s41591-024-03328-5

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  1. Image Challenge. N. Engl. J. Med. https://www.nejm.org/image-challenge (2024).

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Metadata

Authors
Johri, S., Jeong, J., Tran, B. A., Schlessinger, D. I., Wongvibulsin, S., Barnes, L. A., Zhou, H.-Y., Cai, Z. R., Van Allen, E. M., Kim, D., Daneshjou, R., & Rajpurkar, P.
Affiliations
  • Harvard University (US)
  • Washington Hospital (US)
  • Northwestern University (US)
  • University of California, Los Angeles (US)
  • Stanford University (US)
  • Stanford Medicine (US)
Corresponding author
  • Daneshjou, Roxana
  • Rajpurkar, Pranav
ORCID
Venue
Nature Medicine
Publisher
Springer Science and Business Media LLC
Published
2025-01
Volume
31
Issue
1
Pages
77-86
ISSN
  • 1078-8956 (ISSN-L)
  • 1546-170X
DOI
10.1038/s41591-024-03328-5
OpenAlex
W4405995927
Semantic Scholar
597f3cc7c41ba1da58624f79771b26a5fd8de1ba
Cited by
201
References
45
Citations by year
2026: 114 · 2025: 86
FWCI
47.80
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Nature Medicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
large language models, clinical evaluation, doctor-patient interaction, medical history, diagnostic accuracy, GPT-4, GPT-3.5, Mistral, Llama 2, GPT-4V, multimodal assessment, evaluation framework
MeSH terms
Large Language Models, Artificial Intelligence, Communication, Humans, Medical History Taking, Physician-Patient Relations

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