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An evaluation framework for clinical use of large language models in patient interaction tasks
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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“Previous benchmarks like AMIE23, SAPS30, and CRAFT-MD31 focus on dialogue-based evaluations but lack multimodal capabilities and do not simulate real-world biases, tool usage, multilingual, or specialist cases.”
From AgentClinic: a multimodal benchmark for tool-using clinical AI agents (2026) · cites this paper as [31]
In the citing paper
“Although STARD-AI offers a strong foundation for transparent reporting, complementary frameworks such as CRAFT-MD may be better suited for evaluating different forms of AI-driven clinical support 53 .”
From The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence (2025) · cites this paper as [53]
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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
- Published
- 2025-01
- Volume
- 31
- Issue
- 1
- Pages
- 77-86
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- 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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