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Article(id=1321170081091006782, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1321170080579301694, articleNumber=null, orderNo=null, doi=10.11847/zgggws1145056, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1724947200000, receivedDateStr=2024-08-30, revisedDate=1725811200000, revisedDateStr=2024-09-09, acceptedDate=1731254400000, acceptedDateStr=2024-11-11, onlineDate=1792921093484, onlineDateStr=2026-10-25, pubDate=1736438400000, pubDateStr=2025-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792921093484, onlineIssueDateStr=2026-10-25, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792921093484, creator=13701087609, updateTime=1792921093484, updator=13701087609, issue=Issue{id=1321170080579301694, tenantId=1146029695717560320, journalId=1319373219989389464, year='2025', volume='41', issue='1', pageStart='1', pageEnd='128', issueExtLink='null', onlineDate='null', pubDate='1736438400000', pubDateStr='2025-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792921093363, creator='13701087609', updateTime=1792921093363, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=17, endPage=21, ext={EN=ArticleExt(id=1321170081275556159, articleId=1321170081091006782, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Research progress on iatrogenic risks based on Internet of Things medical technology: a systematic literature review, columnId=1321169992624747130, journalTitle=Chinese Journal of Public Health, columnName=Special Report – Public Health Informatization Development and Artificial Intelligence, runingTitle=null, highlight=null, articleAbstract=

This paper aims to explore the application of Internet of Things (IoT) in healthcare under the rapid development of artificial intelligence technology, systematically analyze the resulting iatrogenic risks, and examine their conceptualization, classification, and governance pathways. Through a systematic review of relevant literature and based on Bogner's classification of medical failures, this study categorizes iatrogenic risks into active and latent types and identifies risk factors in the medical application of IoT technology. The research finds that algorithm limitations and cybersecurity issues are the main factors contributing to active iatrogenic risks, while human-machine integration challenges and loose regulatory environments are the primary factors leading to latent iatrogenic risks. While IoT medical technology improves healthcare service efficiency and quality, it also introduces iatrogenic risks. The study indicates that precise efforts in technical security regulatory standards and multi-stakeholder risk avoidance mechanisms are necessary to establish an IoT healthcare risk control system and reduce iatrogenic risks.

, authors=Guangwei DENG1, Shimin LI1, Liang LIANG1, Chengchao ZHOU2, authorsList=Guangwei DENG, Shimin LI, Liang LIANG, Chengchao ZHOU, authorCompany=null, correspAuthors=Guangwei DENG, authorNote=null, correspAuthorsNote=
DENG Guangwei, E-mail:
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本文旨在深入探讨人工智能技术迅猛发展背景下物联网医疗的应用，系统性地分析其引发的医源性风险，并对风险进行概化、分类和治理路径分析。本文通过系统梳理相关文献，并基于Bogner对医疗故障的分类方法，将医源性风险划分为主动型和潜在型两大类，并识别了物联网技术在医疗应用中的风险因素。研究发现，算法局限性和网络安全问题是造成主动型医源性风险的主要因素，人机融合挑战和监管环境宽松是造成潜在型医源性风险的主要因素。物联网医疗技术在提升医疗服务效率和质量的同时，也带来了医源性风险；研究表明，需要在技术安全监管标准和多元互动的风险规避机制上精准发力才能构建物联网医疗风险控制体系，降低医源性风险。

, authors=邓光伟1, *, *, 李世民1, 梁樑1, 周成超2, authorsList=邓光伟, 李世民, 梁樑, 周成超, authorCompany=null, correspAuthors=邓光伟, authorNote=

邓光伟（1990—），讲师，博士，研究方向：公共政策

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邓光伟，E-mail：
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基于物联网医疗技术的医源性风险研究进展
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邓光伟 1, *, * , 李世民 1 , 梁樑 1 , 周成超 2
中国公共卫生 | 专题报道——公共卫生信息化建设与人工智能 2025,41(1): 17-21
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中国公共卫生 |专题报道——公共卫生信息化建设与人工智能 2025 , 41 (1) : 17 -21
基于物联网医疗技术的医源性风险研究进展
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邓光伟1, *, * , 李世民1, 梁樑1, 周成超2
作者信息
  • 1合肥工业大学管理学院公共管理系，合肥 230009
  • 2山东大学公共卫生学院，济南 250012
通讯作者:
邓光伟，E-mail：
作者简介:

邓光伟（1990—），讲师，博士，研究方向：公共政策

Research progress on iatrogenic risks based on Internet of Things medical technology: a systematic literature review
Guangwei DENG1 , Shimin LI1, Liang LIANG1, Chengchao ZHOU2
Affiliations
  • 1Department of Public Administration, School of Management, Hefei University of Technology, Hefei 230009, China
  • 2School of Public Health, Shandong University, Jinan 250012, China
    • Corresponding Author:
    DENG Guangwei, E-mail:
出版时间: 2025-01-10 doi: 10.11847/zgggws1145056
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摘要
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本文旨在深入探讨人工智能技术迅猛发展背景下物联网医疗的应用，系统性地分析其引发的医源性风险，并对风险进行概化、分类和治理路径分析。本文通过系统梳理相关文献，并基于Bogner对医疗故障的分类方法，将医源性风险划分为主动型和潜在型两大类，并识别了物联网技术在医疗应用中的风险因素。研究发现，算法局限性和网络安全问题是造成主动型医源性风险的主要因素，人机融合挑战和监管环境宽松是造成潜在型医源性风险的主要因素。物联网医疗技术在提升医疗服务效率和质量的同时，也带来了医源性风险；研究表明，需要在技术安全监管标准和多元互动的风险规避机制上精准发力才能构建物联网医疗风险控制体系，降低医源性风险。

物联网医疗  /  医源性风险  /  人工智能技术  /  综述

This paper aims to explore the application of Internet of Things (IoT) in healthcare under the rapid development of artificial intelligence technology, systematically analyze the resulting iatrogenic risks, and examine their conceptualization, classification, and governance pathways. Through a systematic review of relevant literature and based on Bogner's classification of medical failures, this study categorizes iatrogenic risks into active and latent types and identifies risk factors in the medical application of IoT technology. The research finds that algorithm limitations and cybersecurity issues are the main factors contributing to active iatrogenic risks, while human-machine integration challenges and loose regulatory environments are the primary factors leading to latent iatrogenic risks. While IoT medical technology improves healthcare service efficiency and quality, it also introduces iatrogenic risks. The study indicates that precise efforts in technical security regulatory standards and multi-stakeholder risk avoidance mechanisms are necessary to establish an IoT healthcare risk control system and reduce iatrogenic risks.

medical Internet of Things  /  iatrogenic risk  /  artificial intelligence technology  /  review
邓光伟, 李世民, 梁樑, 周成超. 基于物联网医疗技术的医源性风险研究进展. 中国公共卫生, 2025 , 41 (1) : 17 -21 . DOI: 10.11847/zgggws1145056
Guangwei DENG, Shimin LI, Liang LIANG, Chengchao ZHOU. Research progress on iatrogenic risks based on Internet of Things medical technology: a systematic literature review[J]. Chinese Journal of Public Health, 2025 , 41 (1) : 17 -21 . DOI: 10.11847/zgggws1145056
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党的二十大报告指出，要大力发展人机融合在内的数智技术使之成为未来关键产业发展的增长引擎，《“十四五”全民医疗保障规划》[1]中亦强调了“提升医疗服务便捷化、协同化程度”和“持续强化数智医疗安全管理”的必要性和重要性。伴随着5G通信技术，人工智能、大数据等新兴技术的发展，物联网进入医疗卫生领域并且在很大程度上能够解决医疗信息不对称，卫生资源不足，传统医疗组织管理效率低下、资源浪费等固有“顽疾”[2]。随着技术的不断进步，物联网的深度融合将为医疗领域带来更加智能、高效的服务，让患者在享受医疗服务同时感受到更多的便捷和安心，也进一步推动医疗卫生事业的健康发展。然而，物联网医疗技术的快速发展也带来了医源性风险，这些风险可能影响患者的健康和安全。本文旨在分析物联网技术在医疗领域的应用现状，探讨其带来的医源性风险，并提出相应的安全管理建议。
1　物联网医疗技术应用现状
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当前，物联网医疗在慢性病管理、远程医疗、临床决策支持方面已经广泛应用，其以智能移动终端和可穿戴仪器两大类设备为基础，以网络通信技术为媒介，向人们提供便捷有针对性的个体化医疗服务[3]。在慢性病防治方面，Gia T N等[4]利用网络边缘的雾计算开发了一种远程健康检测系统，实现了对糖尿病患者葡萄糖、心电图、躯体、温度和生活场景数据的远程实时监控以及特征信息提取，并具备精确信息采集和长续航的功能特点。Shihab A N等[5]在物联网环境中基于递归神经网络（RNN）开发了一种心脏病检测系统，使用ECG 传感器、Arduino和生物医学贴片收集患者数据并将其输入到以循环层作为输入的 RNN 中。近年来，在远程医疗方面专家学者已经开发了很多系统确保物联网技术的远程医疗服务。比如Hamil H等[6]描述了一种使用带有心电图、体温、SpO2、位置、气流和皮肤电等传感器的Arduino和 Raspberry Pi采集和处理数据的远程医疗平台，在物联网环境下对传感器数据进行生物信号诊断和分类，其最佳准确率为99.56%。Shabana N等[7]开发了一种使用面向视觉的VR护目镜和外科医生访问的手部运动采集设备的远程手术物联网平台，由微控制器收集传感器数据，并将其实时传输到两个连接的 ZigBee系统，同时不会中断或丢失数据。在临床决策支持方面，芝加哥大学的研究人员基于决策树模型开发了一种决策支持系统，使用大约60个临床变量（包括人口统计学、生命体征、实验室数据和护理评估）来估计住院患者在48 h内发生2期急性肾损伤和/或接受肾脏替代治疗的概率[8]，该模型[9]在预测急性肾损伤方面实现了0.85～0.86的受试者工作特征曲线下面积，在预测肾脏替代治疗需求方面为0.95～0.96。Moor M等[10]将GMAI模型引入到临床决策支持中，从临床知识图谱和文本资源（如学术出版物、教育教科书、国际指南和当地政策）获取必要的知识后使用语言建模技术根据患者以前的数据预测患者的病情将如何随时间变化。
物联网技术在医疗领域的多方面应用表明其可以通过降低成本、提高效率和诊断准确性等多样的方式改善医疗保健。首先，物联网技术实现了患者生理数据的实时监测与传输，为医生提供了更加准确、及时的诊断依据。其次，物联网技术所构建的远程医疗体系缓解了“看病难”和医疗资源的供给不足问题。此外，物联网技术在助力医疗资源的优化配置，提高医疗服务的效率和质量的同时催生新的医疗服务模式和业态，为医疗行业的可持续发展注入了新的动力。
但是，物联网医疗技术带来巨大效益的同时，风险也伴随而来。一方面，医疗行业致力于实现人类健康，不同于其他运用领域，具有高风险和高专业性的特点，一些关乎患者生命安全的医源性风险极易被忽视。另一方面，物联网医疗仍存在诱发医疗事故风险的潜在因素，如不成熟的开发、算法偏见与过程“黑箱”、技术开发阶段的医疗专业人员的参与度不足等，这些因素增加了医源性伤害的可能性[11]。比如，在线问诊系统方便患者同时多次出现误诊和白诊情况，同一症状多次问诊后诊断结果不一致，对患者身心健康造成严重影响[12]。
2　物联网医疗健康运用的医源性风险
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关于“医源性风险”尚无统一的定义。“医源性”一词源于希腊语，指卫生专业人员的不当行为给病人造成的任何病理改变，从而对病人的健康造成有害的后果[13]。根据世界卫生组织，健康标准包含了生理和心理的健康。因此，本研究把医源性风险概括为在诊断、治疗、康复等全过程的医疗活动中由医方的技术、服务等各种主客观因素造成的失当行为给患者身体和心理带来伤害的风险，这些行为包括了所有与医疗活动有关的行为[14–16]。
Gartner公司的热门度曲线模型描述了新兴技术在市场或领域中的典型发展过程，处于触发阶段的物联网医疗尽管在技术研发与运用方面有光明前景，但也伴随着巨大的障碍和陷阱[17]。一些学者[18–19]认为，当前物联网医疗技术已经到达“膨胀预期的顶峰”，炒作的状态已经远远超过了技术的科学状态，对新技术的临床验证和必要的临床前期准备、论证的不足将带来巨大风险[20]。医源性风险本质上是错误的医疗行为触发的，根据Reason的理论，Bogner将医疗错误分成主动故障（Active failures）和潜在故障（latent failures） [21]。前者强调了院方与患者直接接触做出的不安全行为导致的错误诊疗，后者强调了医疗系统因潜在外界因素触发的风险。本文在识别医源性风险时借助了Bogner的分类方法，将物联网医疗的医源性风险分为两类：主动型医源性风险和潜在型医源性风险，前者由物联网医疗本身产生，直接导致的医源性风险，后者则强调了物联网医疗应用后，外界因素变化间接带来的医源性风险。
2.1　物联网医疗的主动型医源性风险
2.1.1　由物联网医疗的算法局限性带来的医源性风险
算法偏见往往很难避免，数据样本量较小、数据内部存在异质性，多模态医学数据融合算法不成熟等[22]，都会导致算法偏见。训练数据欠佳或者信息不全也会导致算法错误。当训练数据来源对象具有偏好时，开发的应用系统会在面对不同患者群体时“信心满满地”做出错误预测[23]。同时，还存在训练数据被定制化而导致的过度拟合问题。所以，算法在不同医疗机构间的推广会带来医生的决策风险。
可信易懂的算法解释是公众信任和使用AI的前提。算法本身的难以解释性和不透明性加大了公众的担忧[24]，而一些错误的算法和目标函数带来的伤害会更大，不得不让临床医生对的医源性风险保持高度警惕，典型的案例就是IBM的沃森超级计算机推荐了“不安全、不正确”的癌症治疗方法[25]。此外，人工智能识别的患者特征和治疗结果之间的关系只是统计关系，如概率或相关性，而不是因果关系[26–27]。引用概率或统计归纳来解释事件发生原因是无法令人信服的，除非对归纳本身有一个潜在的因果解释[28]。尽管很多人工智能算法在结果和预测方面表现良好[29–30]但它们的不透明性难以让人深入了解它们的内部工作机制，并加剧了问题的复杂性，因为将重要的决策委托给一个无法解释自身的系统，风险显而易见。有学者也指出倾向于功利主义的量化评估忽视或违反了人类的道德原则，带来即使强化算法，风险也无法消除[31]的现象。
2.1.2　由物联网医疗系统的网络安全带来的医源性风险
到2022年，物联网传感器和设备的数量将超过500亿个[32]，端口数量的增加意味着被攻击的风险增加，而物联网设备的低功耗和复杂性限制了传统密码技术的使用，因此物联网医疗的安全性受到了极大的关注[33]。其中，网络攻击的威胁性最大，它包括了4个不同类型的攻击形式：不成功的网络入侵行动；不改变信息完整性的信息窃取；数据的完整性受到破坏或被修改的入侵；网络内无法正常工作，被干扰或直接被操控[34] 。后2种入侵方式就很容易带来医源性风险，错误、不完整和被修改的患者信息会让医师做出错误的判断，比如，无线血糖监测设备可以被入侵者通过用更强的信号广播数据，或被多次攻击和显示错误的读数[35–36]，这会让医生给错误甚至出完全相反的诊断和处理，而医生的处方被篡改和删除也会带来致命后果。类似的问题在远程医疗中更为严重，在远程术中，机器人设备被入侵者控制，将对患者直接展开攻击[37]。攻击者利用ROS漏洞，通过智能自学习恶意软件跟踪机器人手臂的运动，可根据实时操作数据规划攻击策略，能够在外科手术的关键阶段触发攻击，并且攻击者可将恶意行为隐藏起来，逃避系统的安全监控，甚至可将攻击伪装成意外事故[38]。
2.2　物联网医疗的潜在型医源性风险
2.2.1　人机融合过程伴随而来的潜在医源性伤害
物联网技术在深入医疗领域的过程中，虽然带来了便捷和高效，但因其固有的认知非透明性[39]却引发了患者对于医疗服务的信任危机，在医疗活动中增加患者的心理压力。有研究发现多数患者不会把可穿戴智能设备长期使用。作为最重要的利益相关者，患者不了解算法所决定的诊断和治疗方案使得患者很难建立信任[40]。在采用基于智能算法的乳腺癌患者筛查决策辅助系统后，年轻女性不愿接受乳腺癌筛查的人数在增加[41]。另一方面，物联网医疗造成的医患间沟通减少会增加病人的心理压力，间接导致了医源性风险的增大。正确的医疗决策和良好的医患关系不仅仅取决于医生的沟通技巧，还需要临床的互动和协作；不仅通过口头语言，还需要双方非语言信息、情感和自我意识来促进。而物联网医疗的介入可能降低医生和患者的面对面交流的机会，取而代之的是一种远程的交互，甚至于医生直接在智能终端获取患者信息，不与患者进行交流直接做出诊断。
除了引发患者的信任危机之外，物联网技术也对医生形成了较大的冲击。如远程医疗术中，传统医患的信托关系已不复存在，取代它的是整个医疗保健系统与患者的关系，医生个体责任的概念也逐渐淡化[42]。同时，物联网技术在长期使用中会对医师产生“去技能化”影响。在对医疗专家判别效率影响的研究中发现，使用了计算机辅助癌症检测系统后，面对相对复杂的X射线资料，即使鉴别力较强的放射科医师，其诊断灵敏度也会降低[43]。其次，技术可能会增加医生的心理危机。比如依据医学信息做出临床判断和处理决定的能力下降可能导致其自信心的轻微丧失[44]。
2.2.2　专家缺位和宽松监管环境带来的潜在医源性风险
尽管一些医疗监管部门，如美国食品和药品管理局，将新技术软件等视为医疗设备加以管理，但仍未探索出系统性和兼具适应性的具体措施[45]。各国都希望在新的技术竞争中保持领先，给予了较为宽松的监管环境，包括市场监管的程序趋向更为简单，以促进技术创新[46]。另外，算法的知识产权保护[47]，计算水平、方法的进步、软件和架构等系统的创新，以及频繁的互动、高度个性化的应用环境[48]都给监管带来了很大的挑战。因此，食药管理部门对人工智能诊断工具的批准不是整个监管工作的终点，临床运用时监管是最重要的阶段。医疗专业人员在不断监测技术效果方面的作用是至关重要的，他们将决定某个特定的人工智能工具是否对真实世界的患者有用[49]。美国和欧洲的监管平台尚未具备监督其医疗实践的能力[50]；中国也还处在标准制定以及监管探索阶段。
然而，一项针对体重管理的手机应用程序调查研究显示，在近400个被肥胖症患者不同程度使用的应用程序中，仅1%提供了科学的评估证据，有临床医疗专家参与开发的不到0.5%[51]。临床医师在医疗新技术研发过程中的缺位，增加了新技术运用的风险。Tang A等[52]指出科学家、制造商应与医生，及其他关键利益相关者共同合作，推动研发，并确保医生在技术功能定义、技术验证、疾病治疗等过程中处于监督地位和发挥专业作用。广泛采用这些系统的一个决定性前提是在于患者、医生和护士等所有利益相关者的广泛参与，以及清晰责权划分。
3　对策与建议
收起
3.1　建立技术安全监管标准，健全物联网医疗风险控制体系
首先，建立全生命周期的安全管理体系。即从技术的概念阶段到每项产品和服务的生命周期结束阶段，都有技术安全的规范依据。可通过风险调查对潜在的技术风险进行识别、分析与评估，根据智慧程度、潜在技术可控性、可能引发的伤害程度等确定风险等级与安全目标。其次，构建基于患者隐私保护的伦理规范和安全标准。物联网医疗的技术基础在于数据医疗的积极共享和有效开发，隐私保护是患者安全和数据共享的保障。要通过明确物联网实践中的患者权利来进一步完善政策及法律框架，同时，探索运用量子通信、区块链等先进保密技术实现数据的安全采集、传输、储存和开发使用，充分利用技术手段来支持和落实标准和规范。第三，构建基于数据质量管理的安全标准。如前文所述，数据质量决定算法和结论的可靠性、安全性。为了避免使用中的功能退化，在整个应用过程都需要恒定的高质量数据，以防止开发的算法缺陷或退化，要通过数据清洗、集成和标准化的收集、分类等方法来保障数据的一致性、完整性、正确性和可信性。
3.2　充分发挥医方专业作用，强化多元互动的风险规避机制
在技术研发阶段，需培养一支受过交叉学科熏陶的复合型医学人才，使得他们能与数据科学家、计算机工程师等无技术障碍地沟通，以确保开发算法的目标与临床目标保持一致。在临床应用阶段，需要发挥医生和物联网医疗支持系统之间的协同效应，处理好医生在整个临床实践中的角色和关系，重新构建医师胜任力模型，调整能力结构，应对能力不足、衰退、富余及医师培养的机会成本问题，根据角色和关系划分医师在新的医疗实践中的责任。其次，应将物联网医疗纳入到医院临床路径管理框架内。当前的新技术医疗主要用于常见病和多发病的治疗、诊断，而临床路径管理具有标准化、时效性、多专业协作性、成本可控性、技术透明性和结果可测性等特点[53]，有望克服物联网技术在临床应用中的技术障碍，促进由医方参与的技术监管，从而降低医源性风险。

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doi: 10.11847/zgggws1145056
  • 接收时间：2024-08-30
  • 首发时间：2026-10-25
  • 出版时间：2025-01-10
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  • 收稿日期：2024-08-30
  • 修回日期：2024-09-09
  • 录用日期：2024-11-11
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    1合肥工业大学管理学院公共管理系，合肥 230009
    2山东大学公共卫生学院，济南 250012

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