AI summaryⓘ
The authors explain that AI systems used to diagnose patients in sub-Saharan Africa often work without real-time human checks, but the rules to oversee these systems are not well-developed there. They found that existing guidelines mainly focus on doctors and assume strong regulations that aren't always in place in low-resource areas. By studying AI tools used for diseases like tuberculosis and diabetes in Tanzania, Zambia, and Ghana, the authors reveal problems like patients not fully understanding when AI is used and a lack of human control options. They suggest three basic principles to improve these systems: clear consent that informs patients about AI use, guaranteed human override ability, and explanations adapted to local contexts. These ideas aim to provide a practical starting point for better AI oversight where formal laws are missing.
Autonomous AI diagnostic agentseHealthInformed consentAlgorithmic accountabilityExplainable AIHuman overrideSub-Saharan AfricaHealthcare governanceDiagnostic AILow-resource settings
Abstract
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.