Large language models leverage external knowledge to extend clinical insight beyond language boundaries.

ABSTRACT:

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.

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 (KF

SUBMITTER: Wu J 

PROVIDER: S-EPMC11339525 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature