Clinical Task
Clinical task research focuses on developing and evaluating artificial intelligence models, primarily large language models (LLMs) and vision-language models (VLMs), for various healthcare applications. Current efforts concentrate on improving model fairness across diverse demographics, enhancing generalizability and explainability, and addressing challenges like handling long sequences of medical data and integrating multiple data modalities (e.g., text, images). This work is significant because it aims to improve the efficiency and accuracy of clinical workflows, potentially leading to better patient care and more effective healthcare resource allocation.
Papers
ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation
Guangyu Wang, Guoxing Yang, Zongxin Du, Longjun Fan, Xiaohu Li
MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification
Dequan Wang, Xiaosong Wang, Lilong Wang, Mengzhang Li, Qian Da, Xiaoqiang Liu, Xiangyu Gao, Jun Shen, Junjun He, Tian Shen, Qi Duan, Jie Zhao, Kang Li, Yu Qiao, Shaoting Zhang