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人工智能处理医学数据伦理要求的专家共识

李骢^1,2,3, 张晓燕^1,4, 吴云红^1,2,3, 杨晓蕾³, 余华荣^1,5, 金宏波^1,6, 李英博^1,5, 朱朝晖^1,7, 刘瑞^1,8, 刘娜^1,9, 谢轶^1,10, 吕林莉^1,11, 朱心红^1,12, 唐洪^1,13, 李红芳^1,14, 李红丽^1,15, 曾翔俊^1,16, 陈再兴^1,2,17, 范小芳^1,18, 王燕^1,19, 吴枝娟^1,20, 吴遵秋^1,21, 关亚群^1,22, 薛明明^1,23, 罗彬^1,24, 王爱梅^1,2,25, 杨新旺^1,26, 应颖^1,27, 杨秀红^1,28, 黄新忠^1,29, 郎明非^1,30, 陈世民^1,31, 张环环^1,32, 张忠^1,2,33, 黄武^1,34, 徐国标^1,34, 柳嘉琪³⁵, 宋涛³, 肖晶³, 夏云龙³, 管又飞^3,*, 朱亮^1,2,3

¹中国生理学会虚拟仿真与人工智能专业委员会；²辽宁省机能学学会，大连 116041；³大连医科大学，大连 116041；⁴华东师范大学，上海 200241；⁵重庆医科大学，重庆 400016；⁶哈尔滨医科大学机能学实验教学中心，哈尔滨 150081；⁷中国医学科学院北京协和医学院，北京 100730；⁸北京航空航天大学，北京 100191；⁹同济大学附属东方医院，上海 200092；¹⁰四川大学华西医院，成都 610065；¹¹东南大学，南京 214135；¹²华南理工大学/人工智能与数字经济广东省实验室(广州)，广州 510006；¹³大连理工大学，大连 116024；¹⁴兰州大学，兰州 730000；¹⁵陆军军医大学基础医学教学实验中心，重庆 400038；¹⁶首都医科大学，北京 100069；¹⁷中国医科大学医学基础实验教学中心，沈阳 110122；¹⁸温州医科大学基础医学实验国家级实验教学示范中心，温州 325035；¹⁹山西医科大学基础医学院，太原 030001；²⁰福建医科大学基础医学院生理学与病理生理学系，福州350122；²¹贵州医科大学基础医学国家级实验教学示范中心，贵阳 550025；²²新疆医科大学基础医学院，乌鲁木齐 830054；²³内蒙古医科大学，内蒙古呼和浩特 010110；²⁴广西医科大学基础医学院，南宁 530021；²⁵锦州医科大学，锦州 121001；²⁶昆明医科大学基础医学院，昆明 650500；²⁷深圳大学医学部，深圳 518052；²⁸华北理工大学，唐山 063210；²⁹南通大学附属医院，南通 226019；³⁰大连大学基础医学实验教学中心，大连 116622；³¹海南医科大学，海口 571199；³²皖南医学院，芜湖 241002；³³沈阳医学院，沈阳 110034；³⁴成都泰盟软件有限公司，成都 610101；³⁵华为技术有限公司，深圳 518000

摘要

随着人工智能技术的飞速发展，其在医学领域的应用带来了显著的伦理挑战。因而，建立规范、透明、安全的医学数据处理环境，确立医学人工智能的伦理学底线，保障患者权益和数据安全就显得尤为重要。本共识规范了利用人工智能技术进行医学数据处理的各个环节，包括数据的采集、加工、存储、传输、使用和共享，确保医学数据处理符合伦理原则和法律法规，保障患者隐私和数据安全。同时，还强调了合法合规原则、尊重患者隐私原则、保护患者利益原则以及安全可靠原则，并详细讨论了知情同意、数据的使用、知识产权保护、利益冲突和共享等关键性问题。本专家共识的制定，旨在推动人工智能与医学领域的深度融合与持续发展，同时确保在处理医学数据的过程中，人工智能严格遵循相应的伦理规范和法律法规。

关键词： 人工智能; 医学数据; 伦理安全

Expert consensus on ethical requirements for artificial intelligence (AI) processing medical data

LI Cong^1,2,3, ZHANG Xiao-Yan^1,4, WU Yun-Hong^1,2,3, YANG Xiao-Lei³, YU Hua-Rong^1,5, JIN Hong-Bo^1,6, LI Ying-Bo^1,5, ZHU Zhao-Hui^1,7, LIU Rui^1,8, LIU Na^1,9, XIE Yi^1,10, LYU Lin-Li^1,11, ZHU Xin-Hong^1,12, TANG Hong^1,13, LI Hong-Fang^1,14, LI Hong-Li^1,15, ZENG Xiang-Jun^1,16, CHEN Zai-Xing^1,2,17, FAN Xiao-Fang^1,18, WANG Yan^1,19, WU Zhi-Juan^1,20, WU Zun-Qiu^1,21, GUAN Ya-Qun^1,22, XUE Ming-Ming^1,23, LUO Bin^1,24, WANG Ai-Mei^1,2,25, YANG Xin-Wang^1,26, YING Ying^1,27, YANG Xiu-Hong^1,28, HUANG Xin-Zhong^1,29, LANG Ming-Fei^1,30, CHEN Shi-Min^1,31, ZHANG Huan-Huan^1,32, ZHANG Zhong^1,2,33, HUANG Wu^1,34, XU Guo-Biao^1,34, LIU Jia-Qi³⁵, SONG Tao³, XIAO Jing³, XIA Yun-Long³, GUAN You-Fei^3,*, ZHU Liang^1,2,3

¹Virtual Simulation and Artificial Intelligence Committee, Chinese Association for Physiological Sciences；²Liaoning Province Function Science Society, Dalian 116041, China；³Dalian Medical University, Dalian 116041, China；⁴East China Normal University, Shanghai 200241, China；⁵Chongqing Medical University, Chongqing 400016, China；⁶Function Science Experimental Teaching Center, Harbin Medical University, Harbin 150081, China；⁷Chinese Academy of Medical Sciences & Beijing Union Medical College, Beijing 100730, China；⁸Beihang University, Beijing 100191, China；⁹East Hospital Affiliated to Tongji University, Shanghai 200092, China；¹⁰West China Hospital of Sichuan University, Chengdu 610065, China；¹¹Southeast University, Nanjing 214135, China；¹²Artificial Intelligence and Digital Economy Guangdong Province Laboratory (Guangzhou), South China University of Technology, Guangzhou 510006, China；¹³Dalian University of Technology, Dalian 116024, China；¹⁴Lanzhou University, Lanzhou 730000, China；¹⁵Basic Medical Teaching Experimental Center, Army Medical University, Chongqing 400038, China；¹⁶Capital Medical University, Beijing 100069, China；¹⁷Medical Basic Experimental Teaching Center, China Medical University, Shenyang 110122, China；¹⁸Basic Medicine Experimental National Experimental Teaching Demonstration Center, Wenzhou Medical University, Wenzhou 325035, China；¹⁹Basic Medical College, Shanxi Medical University, Taiyuan 030001, China；²⁰Department of Physiology and Pathophysiology, Basic Medical College, Fujian Medical University, Fuzhou 350122, China；²¹Basic Medicine National Experimental Teaching Demonstration Center, Guizhou Medical University, Guiyang 550025, China；²²Basic Medical College, Xinjiang Medical University, Urumqi 830054, China；²³Inner Mongolia Medical University, Hohhot 010110, China；²⁴Basic Medical College, Guangxi Medical University, Nanning 530021, China；²⁵Jinzhou Medical University, Jinzhou 121001, China；²⁶Basic Medical College, Kunming Medical University, Kunming 650500, China；²⁷Shenzhen University Medical School, Shenzhen 518052, China；²⁸North China University of Science and Technology, Tangshan 063210, China；²⁹Affiliated Hospital of Nantong University, Nantong 226019, China；³⁰Basic Medical Experimental Teaching Center, Dalian University, Dalian 116622, China；³¹Hainan Medical University, Haikou 571199, China；³²Wannan Medical College, Wuhu 241002, China；³³Shenyang Medical College, Shenyang 110034, China；³⁴Chengdu Techman Software Co., Ltd., Chengdu 610101, China；³⁵Huawei Technologies Co., Ltd., Shenzhen 518000, China

Abstract

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.

Key words: artificial intelligence; medical data; ethical safety

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通讯作者：管又飞　　E-mail:

引用本文：

李骢, 张晓燕, 吴云红, 杨晓蕾, 余华荣, 金宏波, 李英博, 朱朝晖, 刘瑞, 刘娜, 谢轶, 吕林莉, 朱心红, 唐洪, 李红芳, 李红丽, 曾翔俊, 陈再兴, 范小芳, 王燕, 吴枝娟, 吴遵秋, 关亚群, 薛明明, 罗彬, 王爱梅, 杨新旺, 应颖, 杨秀红, 黄新忠, 郎明非, 陈世民, 张环环, 张忠, 黄武, 徐国标, 柳嘉琪, 宋涛, 肖晶, 夏云龙, 管又飞, 朱亮. 人工智能处理医学数据伦理要求的专家共识[J]. 生理学报 2024; 76 (6): 937-942.

LI Cong, ZHANG Xiao-Yan, WU Yun-Hong, YANG Xiao-Lei, YU Hua-Rong, JIN Hong-Bo, LI Ying-Bo, ZHU Zhao-Hui, LIU Rui, LIU Na, XIE Yi, LYU Lin-Li, ZHU Xin-Hong, TANG Hong, LI Hong-Fang, LI Hong-Li, ZENG Xiang-Jun, CHEN Zai-Xing, FAN Xiao-Fang, WANG Yan, WU Zhi-Juan, WU Zun-Qiu, GUAN Ya-Qun, XUE Ming-Ming, LUO Bin, WANG Ai-Mei, YANG Xin-Wang, YING Ying, YANG Xiu-Hong, HUANG Xin-Zhong, LANG Ming-Fei, CHEN Shi-Min, ZHANG Huan-Huan, ZHANG Zhong, HUANG Wu, XU Guo-Biao, LIU Jia-Qi, SONG Tao, XIAO Jing, XIA Yun-Long, GUAN You-Fei, ZHU Liang. Expert consensus on ethical requirements for artificial intelligence (AI) processing medical data. Acta Physiol Sin 2024; 76 (6): 937-942 (in Chinese with English abstract).