AI, Trust, and the Integrity of Care Delivery
Safe, effective, and accessible care is the promise of AI in healthcare—but without trust, governance, and accountability, that promise fails at scale.
From Trust Covenant to Trust Compact in Healthcare AI
Healthcare has long relied on a covenant of trust between clinicians and patients. As artificial intelligence enters clinical care, that...
Building the AI Social Contract
Artificial intelligence will reshape healthcare only if society agrees on how it should be governed. A new social contract must...
AI’s 1929 Moment: Building HART for Healthcare Safety
Like financial markets before modern regulation, healthcare AI risks scaling failures without structured oversight. The HART framework proposes governance ...
Algorithmovigilance and HART: Building Trust in Healthcare AI
Healthcare must treat algorithms with the same vigilance applied to drugs and medical devices. Continuous monitoring, transparent evaluation, and accountable...
Why Voluntary AI Guidance Falls Short
Voluntary guidelines and ethical principles alone cannot ensure safe adoption of healthcare AI. Durable progress requires enforceable governance structures that...
Who Owns the Data That Trains Healthcare AI?
Healthcare AI depends on vast datasets drawn from patients and clinical systems. Determining who owns, controls, and benefits from this...
Deadly Medication Shortages: Time for a Fix
Medication shortages expose structural weaknesses in healthcare supply chains and regulatory oversight. When essential drugs disappear ...
The Battle Over Drug Costs and Care Equity
The economics of pharmaceuticals influence who receives care and who does not. Rising drug prices and opaque market structures raise...
As Federal AI Guardrails Fall, Healthcare Must Build Its Own
When national regulatory frameworks weaken or lag behind technology, healthcare organizations cannot wait. Institutions must develop their own governance ...
Patient Data, Patient Power: The Next Revolution in Trust
As digital health systems expand, patients increasingly expect transparency and agency over their medical data. Empowering patients with access...
When AI Gets the Facts Backward
AI can augment clinical decision-making, but it cannot replace professional judgment. Maintaining human oversight ensures that algorithms remain tools...
Who Should Lead AI in Healthcare?
Successful adoption of artificial intelligence in healthcare requires leadership that bridges clinical practice, technology, ethics, and governance. Determining who holds...