Health AI Governance and the Future of Responsible Care
Theodore Zanos, Head of the Division of Health AI at Northwell Health, joins Ivan Ruiz, Partner at FINN Partners, for a Global Health and Purpose Summit conversation on health AI governance and the future of responsible care.
The session examines how AI is reshaping healthcare from reactive care toward anticipatory care, from intuition-driven practice toward data-augmented reasoning, and from episodic patient snapshots toward continuous understanding. Zanos explains why responsible adoption requires more than buying tools. Health systems need inventory, security controls, local validation, stakeholder involvement, workflow design, monitoring, and accountable human ownership.
The conversation also explores shadow AI, clinician trust, alert fatigue, vendor transparency, regulatory pressure, and the shared responsibility of health systems, technology companies, and regulators. Its central message is clear. AI in healthcare is not simply an IT project. It is a clinical capability, and responsible care must remain the measure of progress.
Session Intelligence
This session examines health AI governance through the lens of responsible adoption, clinical validation, shadow AI, workflow design, clinician trust, vendor transparency, regulation, and patient outcomes. Its central insight is that AI becomes a clinical capability only when governance turns innovation into safe, useful, and accountable care.
Health AI Governance
Responsible AI starts with inventory, use-case review, data security, validation, and ongoing monitoring.
Clinical Validation
Health systems need local validation against their own patients, workflows, data, and operating realities.
Clinician Trust
AI must respect clinician attention, reduce friction, and support action rather than add alerts and burden.
Responsible Care
Successful AI implementation must be measured by patient outcomes, workflow impact, safety, and usefulness.
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