Institute for AI in Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau Special Administrative Region 999078, China.
Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing 100871, China.
Chin Med J (Engl). 2024 Nov 5;137(21):2529-2539. doi: 10.1097/CM9.0000000000003302. Epub 2024 Sep 19.
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.
近年来，医学人工智能（AI）领域取得了重大进展，基础大语言模型得到了广泛应用。本文探讨了这些模型在医学 AI 中的应用，提出了一种新的分类框架，将其分为疾病特异性、通用领域和多模态模型。文章还讨论了数据获取和扩充等关键挑战，包括数据量、标注、多模态融合和隐私问题。此外，本文还探讨了医学 AI 模型的评估、验证、局限性和监管，强调了它们在医疗保健领域的变革潜力。文章强调了持续改进、数据安全、标准化评估和协作方法的重要性，以确保 AI 能够负责任且有效地融入临床应用。
