Under FDA’s change-control model, the AI device a hospital validates will not stay the one it runs

The week

On January 7, 2025, the Food and Drug Administration published draft guidance titled “Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations”. It described what manufacturers should include in marketing submissions for AI-enabled devices: device descriptions, user interface and labeling, risk assessment, data management, model development and validation, performance monitoring plans and cybersecurity. It encouraged reporting of performance across patient subgroups and suggested a model-card-style summary for users. The draft built on final guidance FDA had issued in December 2024 on predetermined change control plans. Those plans let manufacturers describe in advance how an AI device may be modified after authorization without a new submission. The same week FDA released separate draft guidance on using AI to support regulatory decisions for drugs and biologics.

Hospitals usually read FDA device guidance as the manufacturer’s concern. For AI-enabled devices that habit is costly. Taken together, the January draft and the December 2024 change-control guidance describe a product designed to change after it is sold. A radiology triage tool or a sepsis model may be retrained, recalibrated or have its thresholds adjusted while it sits in a hospital’s workflow, and FDA had built a pathway to allow exactly that.

Earlier articles in this series dealt with the evidence a buyer should demand before purchase and the local validation that should precede go-live. This article concerns what comes after. From the hospital’s side, every post-purchase change to an AI device’s behavior falls into one of three kinds. The manufacturer changes the model within a pre-authorized plan. The manufacturer changes it in ways that need new FDA review. Or the model stays the same while the hospital’s own environment changes around it. Each kind arrives differently and needs its own control.

Pre-authorized changes: updates FDA has already blessed

A predetermined change control plan lets a manufacturer describe in its submission what it may modify, how it will validate each modification and how it will assess impact. Once FDA authorizes the plan, changes within it can ship without a new submission. That is a sensible way to let devices improve. It also means a hospital may validate version A and, months later, run version C without any regulatory event to flag the difference.

The control is contractual and procedural. Contracts should require notice before any modification under a change control plan, with a plain description, the manufacturer’s validation results and updated performance figures, including by subgroup where the guidance encourages them. Internally, a governance group should decide in advance which kinds of change trigger local re-verification. A threshold adjustment on a triage tool used in the emergency department probably does. A change to the user interface probably does not. Version numbers should be visible in the device and recorded alongside its outputs, so that a later review of an adverse event can establish which model produced the result.

Hosting changes the mechanics. When the model runs in the manufacturer’s cloud, an update can take effect overnight with no local installation for IT to schedule or notice. For cloud-hosted devices, the notice clause matters more, and so does a contractual right to delay adoption of a new version for a short verification window. Without it, the hospital’s first sign of a change may be a shift in output patterns that a radiologist notices weeks later.

Changes needing new review: the vendor’s regulatory event

Changes outside the authorized plan require a new FDA submission, so they are less frequent and more visible. The hospital’s exposure here is timing and continuity. A manufacturer may pause features, ship a new product line rather than update the old one, or end support for the authorized version while the new one is under review.

Contracts should address these possibilities. Useful terms include advance notice of planned submissions that affect the hospital’s use and a commitment to support the authorized version for a defined period. The hospital should also keep the right to remain on a validated version until it has verified the new one. For devices embedded in clinical pathways, such as stroke triage or mammography support, the hospital should also have a documented fallback if the device must be withdrawn from use.

Environmental change: same model, different performance

The third kind is the one most likely to be missed, because nothing about the device changes. The hospital replaces a CT scanner, changes an imaging protocol, merges with a system that serves a different population, or alters a workflow so clinicians see the output at a different moment. Any of these can shift the device’s real-world performance. No manufacturer notice will arrive, because the manufacturer does not know.

The control is the hospital’s own change management. Changes to imaging equipment, protocols, documentation templates or patient intake should carry a checklist question: which AI devices consume this input? Where the answer is yes, a short re-verification should follow, such as a silent comparison against clinician reads for a defined sample. Ongoing monitoring adds a second line. Tracking the rate of positive outputs, override rates by clinicians and error reports over time reveals drift whatever its cause. A simple monthly chart per device, reviewed by its owner, is enough to start.

All three kinds need an owner. In many hospitals AI devices are bought by departments, installed by IT and used by clinicians, with no single person accountable for performance over time. Assigning one owner per device, with authority to suspend it and a standing link to the manufacturer, turns these controls into routine practice. The same record also supports the nondiscrimination duty that HHS placed on covered entities for patient care decision support tools, which took effect in May 2025.

With hindsight

The January 2025 draft was still listed by FDA as a draft well into 2026, though FDA placed it on its list of guidance it intended to finalize in fiscal 2026. Change control plans, meanwhile, became a routine feature of AI device submissions, which made the first kind of change more common in practice. FDA faced staff reductions in 2025 that raised questions about review capacity. By mid-2026 the Congressional Research Service noted a somewhat deregulatory turn in FDA’s updates to its clinical decision support and general wellness guidance. The direction of travel therefore put more of the post-purchase burden on hospitals, not less. Of the three kinds of change, environmental change remains the least governed, because it sits inside hospitals’ own equipment and workflow decisions, where AI dependencies are seldom recorded.