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.
@article{arxiv.2409.00097,
title = {Large Language Models for Disease Diagnosis: A Scoping Review},
author = {Shuang Zhou and Zidu Xu and Mian Zhang and Chunpu Xu and Yawen Guo and Zaifu Zhan and Yi Fang and Sirui Ding and Jiashuo Wang and Kaishuai Xu and Liqiao Xia and Jeremy Yeung and Daochen Zha and Dongming Cai and Genevieve B. Melton and Mingquan Lin and Rui Zhang},
journal= {arXiv preprint arXiv:2409.00097},
year = {2025}
}