Large Language Models as Agents in the Clinic

TL;DR

This paper discusses the potential of large language models as interactive agents in healthcare, emphasizing the need for real-world clinical evaluations over traditional benchmarks to ensure effective deployment.

Contribution

It introduces the concept of AI-SCI, a new evaluation framework for assessing LLMs in real-world clinical tasks and interactions.

Findings

Proposes AI-SCI for clinical evaluation of LLMs

Highlights importance of real-world testing over benchmarks

Suggests high-fidelity simulations for interaction assessment

Abstract

Recent developments in large language models (LLMs) have unlocked new opportunities for healthcare, from information synthesis to clinical decision support. These new LLMs are not just capable of modeling language, but can also act as intelligent "agents" that interact with stakeholders in open-ended conversations and even influence clinical decision-making. Rather than relying on benchmarks that measure a model's ability to process clinical data or answer standardized test questions, LLM agents should be assessed for their performance on real-world clinical tasks. These new evaluation frameworks, which we call "Artificial-intelligence Structured Clinical Examinations" ("AI-SCI"), can draw from comparable technologies where machines operate with varying degrees of self-governance, such as self-driving cars. High-fidelity simulations may also be used to evaluate interactions between users and LLMs within a clinical workflow, or to model the dynamic interactions of multiple LLMs. Developing these robust, real-world clinical evaluations will be crucial towards deploying LLM agents into healthcare.