Application of large language models in disease diagnosis and treatment

Abstract

Large language models (LLMs) such as ChatGPT, Claude, Llama, and Qwen are emerging as transformative technologies for the diagnosis and treatment of various diseases. With their exceptional long-context reasoning capabilities, LLMs are proficient in clinically relevant tasks, particularly in medical text analysis and interactive dialogue. They can enhance diagnostic accuracy by processing vast amounts of patient data and medical literature and have demonstrated their utility in diagnosing common diseases and facilitating the identification of rare diseases by recognizing subtle patterns in symptoms and test results. Building on their image-recognition abilities, multimodal LLMs (MLLMs) show promising potential for diagnosis based on radiography, chest computed tomography (CT), electrocardiography (ECG), and common pathological images. These models can also assist in treatment planning by suggesting evidence-based interventions and improving clinical decision support systems through integrated analysis of patient records. Despite these promising developments, significant challenges persist regarding the use of LLMs in medicine, including concerns regarding algorithmic bias, the potential for hallucinations, and the need for rigorous clinical validation. Ethical considerations also underscore the importance of maintaining the function of supervision in clinical practice. This paper highlights the rapid advancements in research on the diagnostic and therapeutic applications of LLMs across different medical disciplines and emphasizes the importance of policymaking, ethical supervision, and multidisciplinary collaboration in promoting more effective and safer clinical applications of LLMs. Future directions include the integration of proprietary clinical knowledge, the investigation of open-source and customized models, and the evaluation of real-time effects in clinical diagnosis and treatment practices.

Figure 1

Studies of the applications of LLMs in diagnosis, treatment, and supporting work. ASA: American Society of Anesthesiologists; COPD: Chronic obstructive pulmonary disease; COVID-19: Coronavirus disease 2019; CT: Computed tomography; ECG: Electrocardiogram; GERD: Gastroesophageal reflux disease; GI: Gastrointestinal; HF: Heart failure; LLMs: Large language models; MCI: Mild cognitive impairment; MLLMs: Multimodal LLMs; NSCLC: Non-small cell lung cancer. Created by BioRender.com and Figdraw (www.figdraw.com).

Figure 2

Roadmap of common techniques for customizing LLMs. LLMs: Large language models; RAG: Retrieval-Augmented Generation; RL: Reinforcement learning.