Evaluating if Ghana's Health Institutions and Facilities Act 2011 (Act 829) Sufficiently Addresses Medical Negligence Risks from Integration of Artificial Intelligence Systems

George Benneh Mensah, P. Dutta
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引用次数: 2

Abstract

With artificial intelligence (AI) integrated increasingly to enhance personalized diagnosis and data-driven treatment recommendations, this analysis examines the legal sufficiency of Ghana’s Health Institutions and Facilities Act 2011 (Act 829) to address medical negligence risks from reliance on AI systems in clinical settings. The CREAC framework structures evaluating gaps where existing health regulations may lack clarity for emerging issues of accountability. Explanation contextualizes the probabilistic nature of AI inferences and how general principles of medical negligence could have ambiguous application currently if erroneous AI contributions result in patient harm. Application to a hypothetical scenario assesses if adequate protections for appropriate integration exist across developers, systems, healthcare facilities, and practitioners under applicable interpretations of existing laws. Finding liability rules insufficient absent targeted AI governance, conclusions recommend amending Act 829 in key areas to codify expectations for responsible innovation and prevent ambiguity in liability. This work carries scientific novelty as one of the first structured jurisdictional analyses internationally of healthcare AI accountability gaps through a legal lens. Practical significance lies in setting the stage for strengthening protections in Ghana through proposed statutory reforms that reduce uncertainty around this crucial area for quality care. The method and recommendations offer a model for modernizing medical negligence law and AI policy amidst ongoing digitization in healthcare worldwide.
评估加纳《2011 年卫生机构和设施法》(第 829 号法案)是否充分解决了人工智能系统集成带来的医疗疏忽风险
随着人工智能(AI)越来越多地用于加强个性化诊断和数据驱动的治疗建议,本分析报告研究了加纳《2011 年卫生机构和设施法》(第 829 号法案)在应对临床环境中依赖人工智能系统所产生的医疗过失风险方面的法律充分性。CREAC 框架旨在评估现有卫生法规在处理新出现的责任问题时可能存在的漏洞。该框架解释了人工智能推断的概率性质,以及如果错误的人工智能推断导致患者受到伤害,医疗过失的一般原则在当前的应用中是如何模糊不清的。在假设场景中的应用,评估了在现有法律的适用解释下,是否存在对开发人员、系统、医疗机构和从业人员进行适当整合的充分保护。结论认为,在缺乏有针对性的人工智能治理的情况下,责任规则是不充分的,因此建议在关键领域修订第 829 号法案,以编纂对负责任创新的期望,并防止责任的模糊性。这项研究具有科学新颖性,是国际上首次通过法律视角对医疗保健领域的人工智能责任差距进行的结构化司法分析之一。其实际意义在于通过建议的法律改革为加强加纳的保护措施奠定基础,从而减少这一关键领域对优质医疗服务的不确定性。该方法和建议为在全球医疗保健数字化进程中实现医疗过失法和人工智能政策的现代化提供了范例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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