AI Governance in Healthcare: Why Human Oversight Is the Foundation of Safe AI

The Promise and the Problem

Artificial intelligence is rapidly transforming healthcare, from diagnostic imaging to predictive analytics and clinical decision support. Large language models like ChatGPT, Gemini, and Claude have given clinicians powerful new tools for documentation, patient education, and medical research. But with this rapid adoption comes a critical question that the healthcare industry cannot afford to ignore: who governs these systems, and how do we ensure they remain safe?

As Dr. Harvey Castro, a board-certified emergency medicine physician and leading voice on AI in healthcare, has consistently argued, the answer lies not in slowing down innovation but in building the right governance structures around it. His perspective is clear and grounded in clinical reality: AI should assist, never replace, human expertise.

Beyond Chatbots: The Shift to Agentic AI

Much of the current conversation around AI in healthcare centers on chatbot-style interactions, where a clinician prompts a model and receives a response. But Dr. Castro points to a more significant shift on the horizon: agentic AI systems that can independently break down complex clinical tasks, interact with electronic health records, and execute workflows within defined boundaries.

This evolution from passive tool to active clinical participant fundamentally changes the governance equation. When AI moves from answering questions to taking actions, the need for robust oversight frameworks becomes urgent. Dr. Castro frames this through a practical lens: the ideal workflow is one where the agent proposes and the human verifies. The physician's role shifts from operator to supervisor, much like an attending overseeing a resident.

Where Human Oversight Is Non-Negotiable

Dr. Castro draws clear boundaries around where AI must never operate independently. These include live resuscitation events requiring immediate human decision-making, high-risk medication prescribing where final authority must remain with a clinician, delivering difficult news to patients and families, and confirming final diagnoses. These red lines are not limitations of the technology. They are ethical imperatives that governance frameworks must encode and enforce.

The Regulatory Challenge

Traditional regulatory frameworks designed for drugs and medical devices are fundamentally mismatched with the nature of AI. Unlike a static device that receives a one-time approval, AI models evolve continuously through retraining on new data. Their performance characteristics can shift in ways that are difficult to predict or audit.

Dr. Castro advocates for adaptive, agile regulatory frameworks that can keep pace with this evolution. Key elements include dynamic continuous monitoring with regular re-certification of AI models, clear data governance policies addressing anonymization, consent, and security, specialized regulatory pathways distinguishing decision-support tools from autonomous systems, mandatory bias detection with explainability requirements, and international harmonization through bodies like the WHO and IMDRF.

Addressing Algorithmic Bias and Health Equity

One of the most pressing governance concerns is algorithmic bias. AI systems trained on non-representative datasets can perpetuate or worsen existing health disparities, delivering suboptimal recommendations for certain demographic groups. Effective governance must mandate diversity in training datasets, require bias detection and mitigation plans, establish regular third-party audits, and ensure transparency in how models reach their conclusions.

Healthcare professionals need to understand not just what an AI system recommends but why. Without this explainability, clinical trust erodes and patient safety is compromised.

Data Privacy in the Age of Clinical AI

Generative AI thrives on large volumes of data, and in healthcare that data is among the most sensitive information that exists. Governance frameworks must address the specific challenges of medical data: robust anonymization techniques that resist re-identification, clear consent management protocols, strict security standards for data collection, use, and sharing, and regular compliance audits.

These are not abstract policy concerns. They directly impact whether patients trust AI-assisted care and whether healthcare organizations can deploy these tools responsibly.

A Global Imperative

AI development and healthcare challenges are inherently global, making international cooperation essential. The World Health Organization has published guidance on the ethics and governance of AI for health, and the International Medical Device Regulators Forum provides a pathway toward harmonized standards. But significant gaps remain between jurisdictions, creating a fragmented landscape that can leave patients unprotected.

The path forward requires building on these foundations while closing gaps through standardized safety and ethical requirements that cross borders.

The Human Element

Perhaps the most important insight from Dr. Castro's body of work is that governance is ultimately about preserving the human element in healthcare. The biggest challenge facing healthcare leaders today, he argues, is navigating AI integration without losing the human touch. The solution is not to fear the technology but to embrace it within structures that keep patients and clinicians at the center.

Effective AI governance in healthcare means establishing ethical frameworks before deployment, educating providers on both the capabilities and limitations of AI tools, building systems that enhance clinical decision-making rather than overwhelming it, and maintaining transparency at every level. The future is not AI versus doctors. It is AI and doctors working together, with clear governance ensuring that collaboration serves patients above all else.