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The FDA has outlined a proposed framework for regulating AI-based software used as a medical device that can continue learning and adapting after reaching the market, centered on what the agency calls the FDA predetermined change control plan.
How the FDA Predetermined Change Control Plan Would Work
The framework would allow manufacturers to submit an upfront plan describing how an algorithm is expected to change over time instead of filing a new premarket submission for every update, according to an FDA discussion paper. This shift could significantly reduce the regulatory burden manufacturers face when updating continuously learning AI algorithms already on the market.
The Two Components of This Plan
The plan includes two components: “SaMD pre-specifications,” which describe the types of modifications a manufacturer anticipates, and an “algorithm change protocol,” which details how those modifications would be made safely. Modifications that fall within an approved plan could be documented and implemented without additional FDA review, while changes outside the plan, such as a shift in intended use, would still require a new premarket submission.
The Regulatory History Behind the FDA Predetermined Change Control Plan
The discussion paper builds on a series of steps the FDA has taken toward regulating AI-enabled devices. A 2023 draft guidance on machine learning-enabled device modifications recommended that manufacturers account for race, ethnicity, disease severity, gender, age and geography in the data used to train and validate their algorithms.
The January 2025 Full-Lifecycle Guidance
A broader draft guidance followed in January 2025, covering the full device lifecycle from design and development through postmarket monitoring. It was the agency’s first guidance to address the entire lifecycle of AI-enabled devices and came as the number of such devices authorized by the FDA surpassed 1,000.
Why This Predetermined Change Control Plan Matters for Continuously Learning AI
Unlike traditional medical devices, which remain static once approved, AI-based software as a medical device can continue learning and adapting after reaching the market, creating a regulatory challenge that traditional premarket submission processes were never designed to address efficiently.
Why Manufacturers Have Sought This Kind of Framework
Requiring a new premarket submission every time an algorithm updates itself based on new data would create significant delays and administrative burden for manufacturers of continuously learning AI devices, making a predetermined change control plan an attractive alternative that still preserves FDA oversight over the boundaries of anticipated changes.
What This Framework Means for Health Systems Deploying AI Devices
Given that more than 1,000 AI-enabled devices have already received FDA authorization, this predetermined change control plan framework could meaningfully affect how quickly health systems see algorithm improvements reach their existing AI tools, since manufacturers operating within an approved plan would no longer need to wait for new premarket clearance before deploying updates. Health system leaders evaluating AI medical devices may want to ask vendors whether their products already operate under, or plan to adopt, this predetermined change control plan structure as part of their procurement due diligence.
Why This Builds Directly on Prior Guidance
This discussion paper’s emphasis on demographic data considerations, tracing back to the 2023 draft guidance, and full lifecycle oversight, building on the January 2025 guidance, suggests the FDA is incrementally assembling a comprehensive regulatory framework for AI devices rather than issuing isolated, disconnected policy documents.
What This FDA Predetermined Change Control Plan Means Going Forward
With the number of FDA-authorized AI-enabled devices already surpassing 1,000, this proposed framework arrives at a moment when the practical need for a more efficient algorithm-update pathway has become increasingly urgent for both manufacturers and the health systems that rely on these tools. Given that changes falling outside an approved plan, such as a shift in intended use, would still require a new premarket submission, this framework maintains meaningful FDA oversight while streamlining the routine, anticipated updates that continuously learning algorithms require.
What to Watch Going Forward
As this discussion paper moves toward potential formal rulemaking, industry observers will likely watch how the FDA defines the boundaries between routine, pre-specified algorithm modifications and more substantial changes still requiring new premarket review. Given the agency’s pattern of building sequential guidance documents since 2023, this FDA predetermined change control plan framework may represent another incremental step toward a more comprehensive AI medical device regulatory structure, with additional guidance or rulemaking likely to follow as the agency continues refining how it oversees this rapidly growing device category.
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