Clinical knowledge in LLMs does not translate to human interactions

TL;DR

This study reveals that while LLMs perform well on medical exams, their assistance in real-world human interactions for medical advice is ineffective, highlighting the need for human-centered testing before deployment.

Contribution

The paper demonstrates that LLMs' high medical knowledge does not translate into effective real-world assistance in healthcare scenarios, emphasizing the importance of human interaction testing.

Findings

LLMs correctly identify conditions in 94.9% of cases when tested alone.

Participants using LLMs identified relevant conditions less than 34.5% of the time.

Participants' ability to determine dispositions was less than 44.2%, not better than control.

Abstract

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.