AI Governance in Healthcare: Protecting Patients From Predictive Bias

About this episode

Can an AI model be mathematically accurate—and still put patients at risk? Dr. Vivian Atud examines AI governance in healthcare, predictive and algorithmic bias, patient safety, human oversight, model drift, clinical validation, and what happens when physicians and AI algorithms disagree.

Discover why healthcare AI can optimize the wrong objective even when the technology performs exactly as designed, why patient autonomy and meaningful human oversight must remain central to clinical decision-making, and why healthcare leaders cannot outsource accountability to algorithms or technology vendors.

Dr. Atud also introduces the PATIENT Governance Framework, a practical approach for healthcare CEOs, boards, clinicians, regulators, insurers, compliance leaders, and technology executives seeking to build safer, fairer, more transparent, and accountable AI-enabled healthcare systems.

• AI governance in healthcare
• Artificial intelligence in healthcare
• Predictive bias and algorithmic bias
• Patient safety and patient autonomy
• Responsible AI in clinical decision-making
• Human oversight of healthcare AI
• AI model validation and model drift
• Healthcare data inequality
• Algorithmic fairness in medicine
• AI accountability and transparency
• FDA and WHO principles for healthcare AI
• Clinical AI risk management
• Physician versus algorithm decision-making
• Healthcare AI governance for executives
• Patient rights, recourse, and AI-assisted care

P — Purpose and Proxy Integrity
What is the AI system actually optimizing—disease, mortality, utilization, cost, readmission, clinical deterioration, eligibility, or resource use?

A — Accountability
Who has clear authority and responsibility for the AI system, including the power to investigate problems, suspend its use, or retire the model?

T — Transparency and Traceability
Can leaders determine how the system was developed, what population it was designed for, its limitations, its current version, and the decisions it influenced?

I — Inclusive Validation
Does the model perform reliably across clinically relevant populations and subgroups rather than simply achieving strong overall accuracy?

E — Escalation, Explanation and Override
What happens when the physician and the algorithm disagree? Clinicians need meaningful pathways to challenge, document, escalate, and override AI recommendations.

N — Notice, Patient Agency and Recourse
When should patients know that AI materially influenced their care, request human review, challenge a decision, or seek another opinion?

T — Tracking After Deployment
Healthcare AI governance must continue after implementation through ongoing monitoring of outcomes, false positives, false negatives, overrides, population changes, and model drift.

1. What consequential AI systems are currently influencing patient care in our organization?
2. What objective is each model actually optimizing?
3. What populations were represented in its development and validation?
4. Do we understand performance across clinically relevant subgroups—not merely overall accuracy?
5. Who has authority to override, suspend, or retire the model?
6. What happens when a patient or clinician challenges an AI-influenced decision?
7. How will we know if the model becomes less safe six months after deployment?

The central question for the future of healthcare may not be how intelligent our machines become, but how accountable human beings remain.

🎙️ The Clarity Mandate with Dr. Vivian Atud

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