Work

Integration of large language models and evidence-based Chinese medicine: A scoping review

Year: 2026

Type: article

Abstract: Background: Large language models (LLMs) have attracted increasing attention in medical research and clinical practice and have been applied to processes related to evidence-based medicine (EBM). However, the extent of their integration with evidence-based Chinese medicine (CM) remains unclear. Methods: We systematically searched PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang Data from 30 November 2022 to 31 January 2026, with supplementary searches conducted in Google Scholar. Studies were included if they applied LLMs to EBM processes within a CM context or investigated LLMs in CM using established evidence-based research designs. Descriptive analysis summarized study characteristics, and findings were mapped according to the evidence ecosystem framework. Results: A total of 12 studies published between 2023 and 2025 were included. Most studies integrated LLMs into different stages of the EBM workflow within a CM context. At the evidence generation stage, studies explored the role of LLMs in identifying research priorities. At the evidence synthesis stage, LLM performance was evaluated in literature screening, data extraction, and risk-of-bias assessment. At the evidence translation stage, studies evaluated the performance of LLMs in guideline-related question answering and recommendation generation. At the evidence implementation stage, LLMs combined with knowledge graphs or retrieval-augmented generation were used to develop intelligent question-answering systems based on CM guidelines or standards. Conclusion: Existing studies suggest that LLMs have begun to be explored across multiple stages of evidence-based CM research and show potential for improving evidence synthesis efficiency and supporting knowledge translation and application. Protocol registration: Open Science Framework (https://osf.io/ztbd5/overview).

Source: Integrative Medicine Research

DOI: 10.1016/j.imr.2026.101349