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人工智能时代名老中医经验数字化传承的思考

Reflections on the Digital Inheritance of Renowned Veteran Traditional Chinese Medicine Practitioners' Expertise in the Artificial Intelligence Era
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摘要 名老中医的临床经验是中医药学术传承的宝贵资源。如何有效地对名中医的经验进行挖掘与传承,对于保存、传播和创新中医学术思想具有重要意义。随着信息技术的飞速发展,数字化转型已成为中医药领域的重要趋势。基于大数据分析技术和人工智能等技术,可促进名老中医药专家术思想凝练和诊疗经验的传承和推广应用;同时,在标准规范的建立、数据采集质量、知识产权保护、医学伦理以及商业化方面有待深入探讨与改进。通过分析当前名老中医经验传承领域的现状,探讨名老中医经验的数字化传承应用方法、趋势与前景展望,以期为名医传承提供更高效的途径。 The clinical experience of renowned veteran traditional Chinese medicine(TCM)practitioners is a precious resource for the academic inheritance of TCM.Effectively mining and transmitting the expertise of these renowned TCM practitioners is of great significance for preserving,disseminating,and innovating TCM academic thought.With the rapid development of information technology,digital transformation has become a major trend in the field of TCM.Technologies based on big data analytics and artificial intelligence can promote the condensation and refinement of academic thoughts and the inheritance,promotion,and application of diagnostic and therapeutic experiences of renowned veteran TCM practitioners.Concurrently,aspects such as the establishment of standards and specifications,data collection quality,intellectual property protection,medical ethics,and commercialization require in-depth discussion and improvement.By analyzing the current state of the field of expertise inheritance from renowned veteran TCM practitioners,this paper explores the application methods,trends,and prospects for the digital inheritance of their expertise,aiming to propose more efficient pathways for the inheritance of renowned TCM practitioners'knowledge.
作者 赵馥 ZHAO Fu(Department of Intensive Care Unit,The First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou Guangdong 510405,China)
出处 《新中医》 2025年第24期167-170,共4页 New Chinese Medicine
基金 广州市科技局市校(院)联合资助项目基础与应用基础研究项目(202201020520)。
关键词 人工智能 名老中医经验 医学人文 大语言模型 数据挖掘 Artificial intelligence Renowned veteran traditional Chinese medicine practitioners'expertise Medical humanities Large language models Data mining
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