From health care AI pilots to proof: Closing the clinical loop with trusted, connected, and auditable AI in 2026

Health care’s AI pilot era is ending as what once felt like science fiction transitions to routine. Models assist with diagnosis, identify risk, and accelerate research. The question now is no longer whether AI can perform, but whether it can perform reliably in real clinical environments. Proof will come from systems that can explain their decisions, integrate with workflows, and improve safely over time.

The data signals the shift. A recent Forbes analysis, citing Menlo Ventures, reports that health care is adopting AI at nearly twice the rate of the broader economy. Yet industry surveys show that only about one in five health care organizations use AI in production today. While adoption increases, gaps remain between pilots that impress and tools that deliver dependable results. These gaps reveal what comes next: Success will depend on technologies that can be governed, audited, and trusted to support decisions at the point of care.

This shift from possibility to responsibility creates a set of simple tests. Can a system show its work. Can it document how it behaves over time. Can it improve safely when it makes mistakes. The answers will separate demonstrations from delivery.

Trust will determine adoption

In health care, trust has always been earned through clarity. Clinicians trust what they can understand and verify. Patients trust the professionals who guide their care. Regulators trust evidence. When a model recommends a treatment or flags a deterioration risk, trust will belong to systems that can show their work at the point of use.

Credibility will belong to platforms that make provenance and governance visible. Health systems and life sciences organizations that can demonstrate data lineage, audit models, and monitor real world performance will complete the transition from pilot to production. Trust will not rest on claims. It will rest on the ability to inspect how a decision was made and how often similar decisions have been correct.

Recent regulatory progress reinforces this expectation. The Office of the National Coordinator’s Decision Support Interventions rule, the Trusted Exchange Framework and Common Agreement for data sharing, and the FDA’s final guidance on Predetermined Change Control Plans collectively establish a foundation for transparent AI. Their technical language serves a simple purpose: To make AI traceable. When clinicians, regulators, and patients can see how a conclusion was reached, confidence follows. This transparency also accelerates compliant discovery by allowing research to move faster while preserving the oversight that protects everyone.

Connected intelligence will reshape the industry

Connection will amplify impact, and progress in health care will be accelerated through systems that learn together. Clinical and research environments are converging through shared data ecosystems. The interoperability standards that once moved imaging across hospital networks now allow molecular, genomic, and clinical data to link across research and delivery workflows.

This convergence is where experimentation meets infrastructure. Forbes, again citing Menlo Ventures, noted that startups often capture early enthusiasm because they move quickly and challenge conventions, but durable impact depends on how experimentation connects with infrastructure. Health systems contribute scale, governance, and safety. The next advance will come from their intersection, where data, workflow, and oversight reinforce one another. Connected intelligence allows discoveries to inform care quickly, and clinical outcomes to refine research in real time.

Trusted exchange is the organizing principle. When data can move securely across institutions, models can be trained on broader and more representative populations. When clinical insights flow back into research, the cycle becomes self-correcting. Organizations that succeed will be those that treat interoperability not as a compliance requirement, but as a strategic asset.

Moonshot for 2026: The self-closing clinical loop

A learning system should connect discovery, delivery, and reimbursement through a continuous cycle. The moonshot for 2026 is a self-closing clinical loop that makes this cycle visible and auditable.

Imagine a discharge process that compiles the summary, reconciles medications, verifies payer requirements, and produces a clean claim before the patient leaves. Imagine that same data stream contributing to real world evidence studies that sharpen future protocols and inform future coverage decisions.

The objective is not automation for its own sake. Automation that cannot be inspected will not earn durable trust. The objective is an auditable loop where insights travel as fast as data itself, and where every automated step can be explained, traced, and improved. A self-closing loop connects the bedside, the back office, and the bench in a way that preserves human judgment while reducing friction and waste.

Closing reflection

AI will not replace human judgment in 2026. It will strengthen organizations that know how to protect it. Adoption is rising each year, yet most healthcare operations remain untouched. The future will not be defined by what AI can do. It will be defined by how responsibly and seamlessly it does it.

Scott R. Schell, PhD, MD, MBA, is Chief Medical Officer at Cognizant