AI 中文总结
本文针对撒哈拉以南非洲自主AI诊断智能体部署中患者认知缺口与监管不足的问题，结合三地部署案例，提出三项基础原则以确立负责任部署的实用最低标准。
AI 中文摘要
自主AI诊断智能体是一类无需强制实时人工审核即可分析患者特定临床数据并生成诊断输出或分诊决策的系统，其在撒哈拉以南非洲的电子健康平台上部署速度已远超监督所需的治理基础设施。尽管大量研究关注医疗AI的问责性、透明度和可解释性，但现有框架大多以临床医生为中心，且假设监管条件在资源匮乏环境中并不普遍存在。文献中大多缺失对患者关于自主智能体的认知差异的以患者为中心的分析，这种差异会导致结构性问责缺口。本文综合了知情同意、算法问责性和可解释AI的现有研究，强调在撒哈拉以南非洲环境中部署AI智能体带来的三个不同挑战。通过三个已记录的部署案例，包括坦桑尼亚的计算机辅助结核病检测、赞比亚的糖尿病视网膜病变与结核病筛查，以及加纳的移动健康聊天机器人分诊，本文证明这些缺口已存在于该地区的活跃部署中。作为回应，本文提出三项基础原则：智能体知情的知情同意、人工覆写作为结构性要求、以及适应环境的可解释性。这一原则组合为正式AI监管仍处于起步阶段的环境中的开发者、卫生系统管理者和政策制定者确立了实用的最低标准。
英文摘要
Autonomous AI diagnostic agents, systems that analyse patient-specific clinical data and produce diagnostic outputs or triage decisions without mandatory real-time human review, are increasingly deployed across eHealth platforms in sub-Saharan Africa at a pace that has outrun the governance infrastructure needed to oversee them. While significant bodies of work address AI accountability, transparency and explainability in healthcare, existing frameworks are largely clinician-centered and assume regulatory conditions that do not uniformly exist in low-resource settings. A patient-centered analysis of the disparity in patient awareness regarding autonomous agents, which results in a structural accountability gap, is mostly missing from the literature. This paper synthesizes existing research on informed consent, algorithmic accountability, and explainable AI to highlight three distinct challenges introduced by deploying AI agents in the sub-Saharan African context. Drawing on three documented deployment cases, including computer-aided tuberculosis detection in Tanzania, diabetic retinopathy and TB screening in Zambia, and mobile health chat-bot triage in Ghana, it demonstrates that these gaps are already present in active deployments across the region. In response, the paper proposes three foundational principles; agent-aware informed consent, human override as a structural requirement and contextually adapted explainability. This triad of principles lays a practical minimum standard for developers, health system administrators and policymakers in contexts where formal AI regulation remains nascent.
Comments10 pages, 4 figures
