Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries

• URL: http://arxiv.org/abs/2305.10163v4
• Date: Tue, 30 Jan 2024 03:58:19 GMT
• Title: Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries
• Authors: Jiageng Wu, Xian Wu, Zhaopeng Qiu, Minghui Li, Yingying Zhang, Yefeng Zheng, Changzheng Yuan and Jie Yang
• Abstract summary: Large Language Models (LLMs) such as ChatGPT and Med-PaLM have excelled in various medical question-answering tasks. We develop a novel in-context learning framework to enhance their performance.
• Score: 48.48630043740588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: $\textbf{Objectives}$: Large Language Models (LLMs) such as ChatGPT and Med-PaLM have excelled in various medical question-answering tasks. However, these English-centric models encounter challenges in non-English clinical settings, primarily due to limited clinical knowledge in respective languages, a consequence of imbalanced training corpora. We systematically evaluate LLMs in the Chinese medical context and develop a novel in-context learning framework to enhance their performance. $\textbf{Materials and Methods}$: The latest China National Medical Licensing Examination (CNMLE-2022) served as the benchmark. We collected 53 medical books and 381,149 medical questions to construct the medical knowledge base and question bank. The proposed Knowledge and Few-shot Enhancement In-context Learning (KFE) framework leverages the in-context learning ability of LLMs to integrate diverse external clinical knowledge sources. We evaluated KFE with ChatGPT(GPT3.5), GPT4, Baichuan2(BC2)-7B, and BC2-13B in CNMLE-2022 and investigated the effectiveness of different pathways for incorporating LLMs with medical knowledge from 7 perspectives. $\textbf{Results}$: Directly applying ChatGPT failed to qualify for the CNMLE-2022 at a score of 51. Cooperated with the KFE, the LLMs with varying sizes yielded consistent and significant improvements. The ChatGPT's performance surged to 70.04 and GPT-4 achieved the highest score of 82.59. This surpasses the qualification threshold (60) and exceeds the average human score of 68.70. It also enabled a smaller BC2-13B to pass the examination, showcasing the great potential in low-resource settings. $\textbf{Conclusion}$: By synergizing medical knowledge through in-context learning, LLM can extend clinical insight beyond language barriers, significantly reducing language-related disparities of LLM applications and ensuring global benefit in healthcare.

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