How AI Agents Can Be Used in Healthcare: Practical Applications, Risks, and Implementation in Resource-Constrained Settings

AI agents, defined as autonomous, goal-directed systems that plan, invoke external tools, and maintain context across multi-step tasks, are moving from research prototypes toward clinical and operational deployment. This paper examines realistic, evidence-based applications across the healthcare value chain, including triage and intake, scheduling and care coordination, clinical decision support, medical documentation, remote patient monitoring, billing automation, and patient education. Drawing on randomized clinical trials and agent benchmarks published in 2025, it argues that the strongest demonstrated value lies in documentation and administrative workflows, where errors are recoverable and clinicians remain in the loop, whereas autonomous clinical reasoning remains limited by hallucination, bias, accountability gaps, and immature regulation. The paper compares agentic systems with traditional clinical AI, evaluates safety, privacy, fairness, and governance risks, and considers implementation challenges in low-resource settings such as sub-Saharan Africa. It concludes that responsible adoption depends less on raw model capability than on governance, local validation, and careful workflow integration.