Internal Medicine DOCTOR
Internal medicine research is increasingly leveraging large language models (LLMs) and other machine learning techniques to augment physician capabilities, focusing on improving diagnostic accuracy, clinical decision support, and patient care. Current research emphasizes developing specialized LLMs fine-tuned on medical data, incorporating techniques like prompt engineering and multi-agent collaboration to enhance trustworthiness and reliability, while also addressing biases and ensuring explainability. These advancements aim to improve physician workflow efficiency, reduce diagnostic errors, and ultimately enhance patient outcomes, though careful consideration of ethical implications and human oversight remains crucial.
Papers
HuatuoGPT, towards Taming Language Model to Be a Doctor
Hongbo Zhang, Junying Chen, Feng Jiang, Fei Yu, Zhihong Chen, Jianquan Li, Guiming Chen, Xiangbo Wu, Zhiyi Zhang, Qingying Xiao, Xiang Wan, Benyou Wang, Haizhou Li
Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning
Tianqing Fang, Zhaowei Wang, Wenxuan Zhou, Hongming Zhang, Yangqiu Song, Muhao Chen