A locked algorithm produces the same output for the same input every time after release. An adaptive algorithm changes its behavior over time as it is exposed to new data, either continuously or in scheduled retraining cycles. Most authorized AI devices are locked: they may be updated, but each update is a planned, validated release. Truly adaptive, continuously learning devices raise questions about how to verify performance, so regulators expect strong controls. In the US, a PCCP lets manufacturers pre-specify the changes an algorithm can undergo, and the EU AI Act requires substantial changes to high-risk systems to go through a new conformity assessment unless predetermined.

Why it matters

FDA's PCCP framework lets manufacturers prospectively define and validate the boundaries of allowable adaptive changes so retraining does not require a new submission for each model update.

Common pitfalls

•Failing to establish clear performance bounds and risk mitigations for adaptive algorithm changes can lead to regulatory non-compliance.
•Treating an adaptive algorithm as a locked algorithm for validation purposes will result in insufficient oversight of its evolving behavior.
•Not documenting the data used for training and retraining, along with corresponding version control, can create significant auditability issues.
•Assuming all adaptive algorithm changes are minor and do not impact safety or effectiveness can lead to patient harm and regulatory scrutiny.

Sources

MDCG Software Guidance · MDCG
FDA - Software as a Medical Device (SaMD) · FDA
FDA - AI/ML-Enabled Medical Devices · FDA

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Common comparison patterns

Regulatory pathway choice - 510(k) vs PMA, 510(k) vs De Novo, Abbreviated vs Special 510(k).
Quality systems - QSR vs ISO 13485, QMSR vs QSR.
Software classification - SaMD vs SiMD, Locked vs adaptive AI.
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