Invisible Agents, Uninformed Patients: Towards Responsible Deployment Of Autonomous AI Diagnostic Agents In Sub-Saharan Africa

Patients in sub-Saharan Africa often don't know they're interacting with AI systems: The paper documents a "structural accountability gap" where autonomous AI diagnostic agents are deployed across eHealth platforms without patients being informed they're receiving AI-generated diagnoses rather than human clinical decisions, as evidenced in case studies from Tanzania, Zambia, and Ghana.

Existing AI accountability frameworks don't fit low-resource healthcare settings: Current guidelines for responsible AI in healthcare are "largely clinician-centered and assume regulatory conditions that do not uniformly exist in low-resource settings," meaning frameworks developed for well-resourced countries cannot be directly applied to sub-Saharan African contexts.

Three practical safeguards are needed immediately: The paper proposes "agent-aware informed consent" (telling patients about AI involvement), "human override as a structural requirement" (ensuring clinicians can override AI decisions), and "contextually adapted explainability" (explaining AI decisions in ways relevant to local healthcare systems) as foundational principles for responsible deployment.

AI deployment is outpacing governance infrastructure: The pace of autonomous AI agent deployment "has outrun the governance infrastructure needed to oversee them," creating a gap between rapid technological rollout and the regulatory systems needed to protect patients.

Real-world deployments already show these problems: Active AI systems for tuberculosis detection, diabetic retinopathy screening, and mobile health triage across the region demonstrate these accountability gaps are not theoretical but already affecting patient care.