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作 者:吴腾 WU Teng(School of Law,Southeast University,Nanjing,Jiangsu,211189,China)
出 处:《医学与社会》2025年第3期9-16,共8页Medicine and Society
基 金:国家社会科学基金资助重大项目,编号为20&ZD189。
摘 要:数字技术与医疗健康深度融合,正深刻影响着医疗服务体系的流程再造、规则重构、功能重塑、生态新建。生成式人工智能技术基于先进知识构建能力,赋能疾病诊疗、药物研发、健康监测、医学科研以及医院运营管理等场域,助力医疗健康数据的精准利用,为智慧医疗建设提供技术支撑和实践基础。然而,生成式人工智能技术为智慧医疗建设带来新机遇的同时,也面临医疗健康数据隐含偏见、医疗模型配置有失公平、医疗算法可解释性和可适应性不足、医疗结果不精准以及医疗输出内容无效等诸多风险。对此,应遵循数据公正、数据信任、普惠共享的公平伦理观念,构建包括医疗健康数据来源管理框架和医疗模型算法解释机制的透明制度体系,形成明确责任主体和加强监管力度基础上的误判责任机制,推动生成式人工智能技术赋能智慧医疗建设的可持续发展。The deep integration of digital technology with health care is profoundly impacting the reengineering of processes,restructuring of rules,reshaping of functions,and the establishment of new ecosystems within the health service system.Generative AI technology based on advanced knowledge construction capabilities,empowers various scenarios such as disease diagnosis and treatment,drug development,health monitoring,medical scientific research,and hospital operation management.It aids in the precise aggregation of health data,laying the technical foundation and practical basis for the development of smart healthcare.However,while generative AI technology brings new opportunities for smart healthcare,it also faces numerous risks,including implicit biases in health care data,inequities in medical model configurations,the interpretability and adaptability of medical algorithms,imprecision in medical outcomes,and the ineffectiveness of medical output content.In response,it is essential to adhere to the ethical principles of data fairness,data trust,and inclusive sharing,to construct a transparent institutional framework for managing medical data sources and explaining medical model algorithms.This should be based on clearly defined responsibilities and strengthened regulatory measures to establish a mechanism for accountability in cases of misjudgment,thereby promoting the sustainable development of smart healthcare empowered by generative AI technology.
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