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Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis

Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis

For instance, Xiong et al [142] created Doctor GLM, a large-scale language model trained on a comprehensive Chinese health care database. Doctor GLM features a prompt designer module that extracts relevant keywords from user input, uses potential disease names as labels, and generates detailed descriptions based on a disease knowledge library. Therefore, Doctor GLM can provide users with accurate and reliable information, including disease symptoms, diagnoses, treatment options, and preventive measures.

Hankun Su, Yuanyuan Sun, Ruiting Li, Aozhe Zhang, Yuemeng Yang, Fen Xiao, Zhiying Duan, Jingjing Chen, Qin Hu, Tianli Yang, Bin Xu, Qiong Zhang, Jing Zhao, Yanping Li, Hui Li

J Med Internet Res 2025;27:e72062

Predicting 30-Day Postoperative Mortality and American Society of Anesthesiologists Physical Status Using Retrieval-Augmented Large Language Models: Development and Validation Study

Predicting 30-Day Postoperative Mortality and American Society of Anesthesiologists Physical Status Using Retrieval-Augmented Large Language Models: Development and Validation Study

When compared to prior deep learning–based approaches, our method significantly outperforms the BERT–deep neural network (DNN) model of Chen et al [43] in mortality prediction. Specifically, our LLM with RAG integration achieved a substantially higher macro F1-score (0.7222, 95% CI 0.6998-0.7446) compared to their model (0.307, 95% CI 0.269-0.342), indicating a superior balance between precision and recall across both mortality and survival classes.

Ying-Hao Chen, Shanq-Jang Ruan, Pei-fu Chen

J Med Internet Res 2025;27:e75052