AI agents are increasingly embedded in clinical workflows, but experts warn that without guardrails they can jeopardize patient safety and privacy. The article underscores rigorous validation, ongoing monitoring, and a human-in-the-loop approach to ensure AI recommendations are explainable and auditable. It also flags risks around data leakage, model drift, and high-stakes use cases such as triage, diagnostics, and treatment planning.
Healthcare providers should establish governance frameworks, clear accountability for AI-driven decisions, and continuous performance surveillance aligned with regulatory and ethical standards. The piece argues for transparent deployment, bias mitigation, and robust incident reporting to ensure AI augments clinicians without eroding trust. Experts also call for cross-disciplinary oversight, vendor risk management, and interoperability with existing health IT systems. Clear escalation paths ensure accountability when AI errs.
