Is Your AI Safe? Ask the Patient, Not the Algorithm.

The Personal Imperative of Patient Safety

In the quiet waiting rooms of the Mayo Clinic, I have watched the slow transformation of medicine unfold, knowing firsthand that AI's promise comes with a profound peril. My journey from a strategic executive to an AI Governance professional began with a near-fatal sentinel event—a gross medical error that a simple algorithmic check, a basic governance safeguard, would have prevented. This experience taught me that patient safety is not an afterthought; it is the fundamental litmus test of AI governance.

The Governance Gap in Real-World Safety

For too long, the healthcare industry has measured AI safety in controlled laboratory settings. Yet, the evidence is stark: AI systems often experience performance degradation—or "model drift"—when deployed in real-world clinical environments due to shifting patient characteristics and evolving protocols. This means an AI system that was safe and accurate during validation can become unsafe and inaccurate over time.

This systematic decay is the direct result of inadequate governance. As a patient, I demand to know: Is your AI safe today, or was it only safe on the day the FDA cleared it?

The FDA's Total Product Lifecycle (TPLC) approach was developed precisely because AI is dynamic, requiring continuous monitoring. The clinician cannot be the only firewall; robust AI assurance must be baked into the system through continuous monitoring and audit trails that identify degradation before it harms a patient. We must prioritize the patient's right to safety, ensuring the human-in-the-loop is always an effective fail-safe, not a mere formality.

About Dan Noyes

Dan Noyes operates at the intersection of healthcare AI strategy and governance. After 25 years leading digital marketing strategy, he is transitioning his expertise to healthcare AI, driven by his experience as a chronic care patient and his commitment to ensuring AI serves all patients equitably. Dan holds AI certifications from Stanford, Wharton, and Google Cloud, grounding his strategic insights in comprehensive knowledge of AI governance frameworks, bias detection methodologies, and responsible AI principles. His work focuses on helping healthcare organizations implement AI systems that meet both regulatory requirements and ethical obligations—building governance structures that enable innovation while protecting patient safety and advancing health equity