The Real Risk Is Not AI Failure but Success Without Oversight

There is growing consensus that AI in healthcare will transform how care is delivered, documented, billed, and evaluated. But there is much less consensus around how it should be governed. As hospitals rush to embed generative and predictive models into everyday workflows, few have a systemwide strategy to ensure fairness, transparency, and accountability are more than just talking points.

The Q&A with Jim Younkin, MBA, senior director at Audacious Inquiry, made this gap uncomfortably clear. Younkin’s five-part framework such as accountability, fairness, transparency, human oversight, and privacy, and offers a roadmap for building trust at scale. But it also raises the question: why have so few health systems operationalized these principles?

Part of the issue is velocity. According to a 2024 Accenture Digital Health report, over 80 percent of health executives said they planned to expand AI deployments within the year. But fewer than half had a cross-functional governance framework in place. That mismatch creates risk, not just legal or compliance risk, but reputational risk in a system that depends on public trust.

The danger is not that AI will fail clinically. It is that it will succeed operationally without being subjected to meaningful oversight.

We have seen this before. Algorithms deployed for population health analytics have shown measurable bias across racial lines, as highlighted in a landmark 2019 study published in Science, where a widely used risk score system systematically underestimated the needs of Black patients. This was not a failure of intent. It was a failure of structural accountability because no one thought to define fairness before the system went live.

That is why Younkin’s insistence on predefined fairness criteria is so important. AI systems in healthcare are only as just as the training data and review processes they are built on. If those criteria are vague or absent, the algorithm will replicate whatever inequities exist in the claims, documentation, or utilization patterns it learns from.

What comes next must be deliberate.

One place to look for guidance is the evolving regulatory posture of the federal government. In 2025, the Office of the National Coordinator for Health IT and CMS have both signaled greater scrutiny of AI tools embedded in certified health IT. The ONC’s Health IT Certification Program is already moving toward a risk-based classification of decision support algorithms, and CMS is expected to follow with new rules that tie reimbursement eligibility to auditable AI safety protocols.

This is not a hypothetical future. It is an emerging compliance environment that will reward readiness and punish retrofitting.

But policy is not enough. Health systems must lead with their own infrastructure. That means designing governance teams with interdisciplinary authority, embedding equity assessments into procurement and implementation, and maintaining live documentation of how AI tools behave across patient populations.

The question for provider executives is no longer whether AI can improve productivity. It can. The question is whether your system can defend its use under scrutiny, from regulators, from patients, or from your own ethics committee.

That demands traceability. It demands transparency not only for technical teams, but for clinicians and patients as well. It demands knowing when and how human oversight kicks in, and who is accountable when it fails.

The future of AI in healthcare will not be driven solely by innovation. It will be shaped by trust. And trust is not a byproduct of performance. It is an outcome of design.

The organizations that take this seriously today will lead tomorrow, not just in operational efficiency, but in credibility, compliance, and care quality.