Large Language Models for Disease Diagnosis: A Scoping Review

TL;DR

This paper provides a comprehensive review of how large language models are applied to disease diagnosis, analyzing current methods, data, and evaluation techniques, and offering future research directions.

Contribution

It is the first extensive review summarizing LLM applications, techniques, and evaluation methods in disease diagnosis, highlighting gaps and future opportunities.

Findings

LLMs are increasingly used across various diseases and clinical specialties.

Current evaluation methods for LLM-based diagnosis vary widely.

Limitations include data quality issues and lack of standardized benchmarks.

Abstract

Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such as the diseases and clinical data to which LLMs have been applied, the LLM techniques employed, and the evaluation methods used. In this article, we perform a comprehensive review of LLM-based methods for disease diagnosis. Our review examines the existing literature across various dimensions, including disease types and associated clinical specialties, clinical data, LLM techniques, and evaluation methods. Additionally, we offer recommendations for applying and evaluating LLMs for diagnostic tasks. Furthermore, we assess the limitations of current research and discuss future directions. To our knowledge, this is the first comprehensive review for LLM-based disease diagnosis.