Clinical knowledge in LLMs does not translate to human interactions
Global healthcare providers are exploring use of large language models (LLMs)
to provide medical advice to the public. LLMs now achieve nearly perfect scores
on medical licensing exams, but this does not necessarily translate to accurate
performance in real-world settings. We tested if LLMs can assist members of the
public in identifying underlying conditions and choosing a course of action
(disposition) in ten medical scenarios in a controlled study with 1,298
participants. Participants were randomly assigned to receive assistance from an
LLM (GPT-4o, Llama 3, Command R+) or a source of their choice (control). Tested
alone, LLMs complete the scenarios accurately, correctly identifying conditions
in 94.9% of cases and disposition in 56.3% on average. However, participants
using the same LLMs identified relevant conditions in less than 34.5% of cases
and disposition in less than 44.2%, both no better than the control group. We
identify user interactions as a challenge to the deployment of LLMs for medical
advice. Standard benchmarks for medical knowledge and simulated patient
interactions do not predict the failures we find with human participants.
Moving forward, we recommend systematic human user testing to evaluate
interactive capabilities prior to public deployments in healthcare.
681039b2b02c157249d046b0
Andrew M. Bean
admin_assigned
2025-04-29T12:19:18.934Z
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/avatars/217ae547d6460e65c6d2a23012741830.svg
Andrew Bean
user
ambean
681039b2b02c157249d046b1
Rebecca Payne
681039b2b02c157249d046b2
Guy Parsons
681039b2b02c157249d046b3
Hannah Rose Kirk
admin_assigned
2025-04-29T12:19:41.886Z
6255baa66b1f65958caf026c
https://cdn-avatars.huggingface.co/v1/production/uploads/1649842998853-6255baa66b1f65958caf026c.jpeg
Hannah Rose Kirk
user
HannahRoseKirk
681039b2b02c157249d046b4
Juan Ciro
admin_assigned
2025-04-29T12:19:50.597Z
6538526e83bcfe980bfe910c
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juan ciro
user
Chronoszoldyck11
681039b2b02c157249d046b5
Rafael Mosquera
claimed_verified
2025-05-28T19:14:55.371Z
624c746d459c48438cc79601
/avatars/f7a9ecd3a044c5ddaaa46877a83bb955.svg
Rafael Mosquera
user
remg1997
681039b2b02c157249d046b6
Sara Hincapié Monsalve
admin_assigned
2025-04-29T12:20:12.417Z
66aa5c0703b67c04b2cae65f
/avatars/c9406874fdbe3eb0c0269564df3f1038.svg
Sara Hincapié Monsalve
user
sahimo
681039b2b02c157249d046b7
Aruna S. Ekanayaka
681039b2b02c157249d046b8
Lionel Tarassenko
681039b2b02c157249d046b9
Luc Rocher
claimed_verified
2025-04-29T12:13:43.279Z
66291b9555d7c289634d75c0
https://cdn-avatars.huggingface.co/v1/production/uploads/66291b9555d7c289634d75c0/-h0MYYoV9G0jMCw4KBbBG.jpeg
Luc Rocher
user
cynddl
681039b2b02c157249d046ba
Adam Mahdi
claimed_verified
2025-04-29T12:13:51.311Z
673cf9c93af47d1d2b6584c9
/avatars/0792921a8adaa1493255abb0b5aaa4f1.svg
Adam Mahdi
user
ammaox
Clinical knowledge in LLMs does not translate to human interactions
Global healthcare providers are exploring use of large language models (LLMs)
to provide medical advice to the public. LLMs now achieve nearly perfect scores
on medical licensing exams, but this does not necessarily translate to accurate
performance in real-world settings. We tested if LLMs can assist members of the
public in identifying underlying conditions and choosing a course of action
(disposition) in ten medical scenarios in a controlled study with 1,298
participants. Participants were randomly assigned to receive assistance from an
LLM (GPT-4o, Llama 3, Command R+) or a source of their choice (control). Tested
alone, LLMs complete the scenarios accurately, correctly identifying conditions
in 94.9% of cases and disposition in 56.3% on average. However, participants
using the same LLMs identified relevant conditions in less than 34.5% of cases
and disposition in less than 44.2%, both no better than the control group. We
identify user interactions as a challenge to the deployment of LLMs for medical
advice. Standard benchmarks for medical knowledge and simulated patient
interactions do not predict the failures we find with human participants.
Moving forward, we recommend systematic human user testing to evaluate
interactive capabilities prior to public deployments in healthcare.
LLMs perform well in medical licensing exams but fail to provide accurate medical advice to the public due to challenges in user interaction.
large language models LLMs GPT-4o Llama 3 Command R+ medical licensing exams user interactions