Healthcare leaders have heard lofty promises about AI: better diagnoses, less documentation, fewer delays, more capacity, and so on. The promise is universal enough that it featured in a recent episode of HBO’s series The Pitt. Many leaders are treating adoption like a procurement decision. They can just pick a tool, run a pilot, and show an easy win.

Hospitals, however, present a messier experience. No matter how well a model performs on a slide deck, if clinicians hesitate to act on its advice, problems manifest and rapidly multiply. When anything goes wrong, accountability can rapidly become a blurry mess. As a result, we’re seeing a widening gap between technological promise and clinical reality. A 2025 viewpoint in JMIR Formative Research calls out this dynamic: uptake remains limited when technology, people, and ethics are treated as separate tracks and not one integrated operating system.

It isn’t a question of whether or not AI can work. It is a question of whether the organization is built to use it safely. The most important question for CEOs, CHROs, and Chief Medical Officers is not “What can AI do?” It is “What will we enable it to do, reliably, under clinical pressure, without compromising equity or trust?”

Where to start

Start with where AI has shown improvements. Diagnostics and imaging have clearer inputs, clearer outputs, and tighter feedback loops. Predictive models for inpatient deterioration can be powerful if and when you embed them into real decisions, not just dashboards. Even in this “best case” category, however, the variation is sobering.

A major 2024 JAMA Network Open cohort study compared six early warning scores, including proprietary AI tools, and found wide differences in accuracy. One AI score (eCART) remembers more true deterioration events with fewer false alarms, while a widely used proprietary score (Epic’s Deterioration Index, EDI) underperformed a simpler non-AI score (NEWS). The authors explicitly call for more transparency and oversight. That is the first reality check: “AI” is a category label for software and technology, not a guarantee of better clinical signal.

Then there is implementation. A 2024 systematic review in JAMIA examining the real-world implementation of machine-learning deterioration models found a recurring pattern: the pathway from algorithm to improved outcomes breaks when workflows and reporting are inconsistent, bias is moderate-to-high, and performance across implementation stages is not uniformly measured. In other words, hospitals are deploying models without a consistent operational playbook for proving they work in their context.

Putting clinicians at the center

To put clinicians appropriately at the center of the conversation, we should first acknowledge that the trust problem is not philosophical but operational. AI embedded in clinical decision support can reduce harm, but it can also create harm through alert fatigue, poor training, and unclear integration into clinical judgment. A 2024 JAMA Health Forum piece on AI and patient safety discusses this in depth. Of particular note is the gap in medical education for integrating AI outputs into decisions. If clinicians feel that a new tool increases their cognitive load, they will tend to ignore it and carry on without its use.

In 2025, Nature Medicine published a pragmatic cluster-randomized trial of the CONCERN early warning system. This system is modeled on nursing surveillance patterns and is displayed in the EHR. The study found a statistically significant reduction in inpatient deterioration risk. CONCERN did not work because it was “smarter,” but because it mirrored how nurses already detect risk and made the signal usable. That is enablement, not replacement.

Regulatory landscape changes

Finally, the regulatory environment is forcing the same conclusion: governance and transparency are now part of the job. The ONC’s HTI-1 final rule introduced algorithm transparency requirements for predictive decision support interventions in certified health IT. CMS is pushing interoperability and cleaner prior authorization processes, with major provisions rolling into 2026–2027 implementation timelines, and FDA’s 2025 final guidance on Predetermined Change Control Plans (PCCPs) reflects the new expectation that learning systems must change in controlled, documented, reviewable ways. Healthcare systems are not simply adopting AI protocols and technologies. Regulatory bodies are involved in ensuring the safety and efficacy of these tools as well.

Speeding up medicine isn't replacing clinicians

AI is not replacing clinicians, but it is exposing gaps in data integrity, interoperability, training, and leadership readiness. The winners will not be the systems with the most pilots; they will be the systems that can operationalize safe action.

Some health systems are moving faster, and it is tempting to conclude the laggards simply lack urgency. In advanced academic centers, or in narrow, well-scoped use cases with strong informatics teams, AI can show cleaner ROI earlier.

But those examples reinforce the core point: readiness drives results. Transparency and nondiscrimination expectations are rising, not falling. HHS’s Section 1557 nondiscrimination updates explicitly point to “patient care decision support tools” and require reasonable steps to identify and mitigate discriminatory impacts. If your foundations are weak, speed is not strategy. It is exposure.

The problem isn't AI. It's enablement.

Healthcare does not have an AI problem. It has an enablement problem.

Nobody will solve the gap between promise and reality by buying another model. You can only solve that gap by building the conditions where a model can be trusted, acted on, and audited. That means data integrity you can defend, interoperability that supports real workflows, and training that helps clinicians question AI confidently, not comply blindly. Governance must make equity, safety, and accountability explicit.

If you want to cut through the hype cycle in 2026, stop asking what AI will do “eventually.” Ask what your system can safely operationalize today, under real clinical pressure, with regulators watching and patients depending on it.

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