Governance Risks of Autonomous AI in Healthcare Recommendation Systems
Abstract
The integration of autonomous artificial intelligence into healthcare recommendation systems represents a paradigm shift in clinical decision support, transitioning from static heuristic tools to dynamic, agentic infrastructures. While these systems promise unprecedented precision in personalized medicine and resource optimization, they introduce profound governance risks that transcend traditional bioethical frameworks. This paper provides a comprehensive system-level analysis of the structural trade-offs and socio-technical vulnerabilities inherent in autonomous healthcare AI. We explore the tensions between algorithmic robustness and clinical adaptability, emphasizing the risks of hidden misalignment where systems optimize for administrative efficiency at the expense of patient-centric outcomes. Central to this inquiry is the challenge of internal governance; we argue that current regulatory models focus excessively on external constraints, neglecting the latent reasoning traces that dictate autonomous behavior. The research examines the infrastructure requirements for deploying these systems, the sustainability of computational loads in hospital settings, and the fairness implications of biased training data across diverse populations. By synthesizing perspectives from systems engineering, public policy, and medical ethics, the paper elucidates the necessity of governance-by-design. We analyze how policy must evolve to address the missing dimension of internal alignment, ensuring that autonomous agents remain resilient to environmental drift and adversarial pressures. The conclusion offers a strategic roadmap for institutionalizing accountability, suggesting that the future of healthcare AI depends on our ability to embed normative constraints directly into the architectural substrate of medical decision-making agents.
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