Leveraging foundation and large language models in medical artificial intelligence  

作　　者：Io Nam Wong Olivia Monteiro Daniel T.Baptista-Hon Kai Wang Wenyang Lu Zhuo Sun Sheng Nie Yun Yin 

机构地区：[1]Institute for AI in Medicine,Faculty of Medicine,Macao University of Science and Technology,Macao Special Administrative Region 999078,China [2]Department of Big Data and Biomedical AI,College of Future Technology,Peking University,Beijing 100871,China [3]Department of Ophthalmology,The Third People’s Hospital of Changzhou,Changzhou,Jiangsu 203001,China [4]Institute for Advanced Study on Eye Health and Diseases,Wenzhou Medical University,Wenzhou,Zhejiang 325027,China [5]Division of Nephrology,National Clinical Research Center for Kidney Disease,State Key Laboratory of Organ Failure Research,Nanfang Hospital,Southern Medical University,Guangzhou,Guangdong 510515,China [6]Faculty of Health and Wellness,Faculty of Business,City University of Macao,Macao Special Administrative Region 999078,China

出　　处：《Chinese Medical Journal》2024年第21期2529-2539,共11页中华医学杂志(英文版)

基　　金：supported by grants from the Macao Science and Technology Development Fund(No.0069/2021/AFJ);the Macao University of Science and Technology Faculty Research Grants(No.FRG-22-022-FMD).

摘　　要：Recent advancements in the field of medical artificial intelligence(AI)have led to the widespread adoption of foundational and large language models.This review paper explores their applications within medical AI,introducing a novel classification framework that categorizes them as disease-specific,general-domain,and multi-modal models.The paper also addresses key challenges such as data acquisition and augmentation,including issues related to data volume,annotation,multi-modal fusion,and privacy concerns.Additionally,it discusses the evaluation,validation,limitations,and regulation of medical AI models,emphasizing their transformative potential in healthcare.The importance of continuous improvement,data security,standardized evaluations,and collaborative approaches is highlighted to ensure the responsible and effective integration of AI into clinical applications.

关 键 词：Artificial intelligence Foundation model Large language model MULTI-MODAL Data security Medical AI Segmentanchoring model ChatGPT Disease-specific model General-domain model Data privacy HALLUCINATION Data annotation 

分 类 号：H31[语言文字—英语]