Large Language Models for Disease Diagnosis: A Scoping Review
This review synthesizes existing research to guide clinicians and researchers in applying and evaluating LLMs for diagnostic tasks, but it is incremental as it compiles and analyzes prior work without new experimental results.
The authors conducted a scoping review to provide a holistic view of how large language models (LLMs) are applied in disease diagnosis, addressing gaps in understanding diseases, data, techniques, and evaluation methods, and offering recommendations and future directions.
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.