title: Clinical knowledge in LLMs does not translate to human interactions
summary: 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.
paper_authors._id: 681039b2b02c157249d046b0
paper_authors.name: Andrew M. Bean
paper_authors.status: admin_assigned
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paper_authors.fullname: Andrew Bean
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paper_authors.user: ambean
paper_authors._id: 681039b2b02c157249d046b1
paper_authors.name: Rebecca Payne
paper_authors._id: 681039b2b02c157249d046b2
paper_authors.name: Guy Parsons
paper_authors._id: 681039b2b02c157249d046b3
paper_authors.name: Hannah Rose Kirk
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:19:41.886Z
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paper_authors.fullname: Hannah Rose Kirk
paper_authors.type: user
paper_authors.user: HannahRoseKirk
paper_authors._id: 681039b2b02c157249d046b4
paper_authors.name: Juan Ciro
paper_authors.status: admin_assigned
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paper_authors._id: 6538526e83bcfe980bfe910c
paper_authors.avatarUrl: /avatars/19ccfd1d16a1426c4de418f9844f21ee.svg
paper_authors.fullname: juan ciro
paper_authors.type: user
paper_authors.user: Chronoszoldyck11
paper_authors._id: 681039b2b02c157249d046b5
paper_authors.name: Rafael Mosquera
paper_authors.status: claimed_verified
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paper_authors._id: 624c746d459c48438cc79601
paper_authors.avatarUrl: /avatars/f7a9ecd3a044c5ddaaa46877a83bb955.svg
paper_authors.fullname: Rafael Mosquera
paper_authors.type: user
paper_authors.user: remg1997
paper_authors._id: 681039b2b02c157249d046b6
paper_authors.name: Sara Hincapié Monsalve
paper_authors.status: admin_assigned
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paper_authors._id: 66aa5c0703b67c04b2cae65f
paper_authors.avatarUrl: /avatars/c9406874fdbe3eb0c0269564df3f1038.svg
paper_authors.fullname: Sara Hincapié Monsalve
paper_authors.type: user
paper_authors.user: sahimo
paper_authors._id: 681039b2b02c157249d046b7
paper_authors.name: Aruna S. Ekanayaka
paper_authors._id: 681039b2b02c157249d046b8
paper_authors.name: Lionel Tarassenko
paper_authors._id: 681039b2b02c157249d046b9
paper_authors.name: Luc Rocher
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-29T12:13:43.279Z
paper_authors._id: 66291b9555d7c289634d75c0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66291b9555d7c289634d75c0/-h0MYYoV9G0jMCw4KBbBG.jpeg
paper_authors.fullname: Luc Rocher
paper_authors.type: user
paper_authors.user: cynddl
paper_authors._id: 681039b2b02c157249d046ba
paper_authors.name: Adam Mahdi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-29T12:13:51.311Z
paper_authors._id: 673cf9c93af47d1d2b6584c9
paper_authors.avatarUrl: /avatars/0792921a8adaa1493255abb0b5aaa4f1.svg
paper_authors.fullname: Adam Mahdi
paper_authors.type: user
paper_authors.user: ammaox
paper_title: Clinical knowledge in LLMs does not translate to human interactions
paper_summary: 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.
paper_ai_summary: LLMs perform well in medical licensing exams but fail to provide accurate medical advice to the public due to challenges in user interaction.
paper_ai_keywords: large language models LLMs GPT-4o Llama 3 Command R+ medical licensing exams user interactions