From text to image:challenges in integrating vision into ChatGPT for medical image interpretation  

作　　者：Shunsuke Koga Wei Du 

机构地区：[1]Department of Pathology and Laboratory Medicine,Hospital of the University of Pennsylvania,Philadelphia,PA,USA

出　　处：《Neural Regeneration Research》2025年第2期487-488,共2页中国神经再生研究（英文版）

摘　　要：Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).

关 键 词：IMAGE DIAGNOSIS TEXT 

分 类 号：R319[医药卫生—基础医学] TP391.41[自动化与计算机技术—计算机应用技术]