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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Software & AI

Predetermined Change Control Plan(PCCP)

In one line

FDA mechanism to pre-authorize specific modifications to AI/ML-enabled devices.

Definition

A Predetermined Change Control Plan (PCCP) is a document, included in a marketing submission, that describes planned modifications to an AI/ML-enabled device, the methods used to develop, validate, and implement those modifications, and the impact on the device. A PCCP has three parts: a description of modifications, a modification protocol (data management, retraining, performance evaluation and update procedures) and an impact assessment. Once authorized, changes made in line with the plan do not need a new submission. FDA finalized its guidance on PCCPs for AI-enabled device software functions in December 2024, and section 515C of the FD&C Act, added in 2022, gives FDA the authority to approve them for all devices.

Why it matters

PCCPs let manufacturers update AI/ML models within an authorized envelope without a new submission, while preserving safety and effectiveness controls.

Common pitfalls

•Misunderstanding that a PCCP grants blanket approval for any change; only modifications explicitly covered and detailed in the authorized plan are permitted without new submission.
•Failing to adequately specify validation methods or performance evaluation procedures in the PCCP, leading to potential FDA rejection or challenges during post-market implementation.
•Assuming PCCPs eliminate all regulatory oversight for AI/ML updates; significant deviations or changes outside the scope of the authorized plan still require new regulatory review.
•Underestimating the level of detail required in the PCCP, particularly regarding data management and retraining protocols, which are crucial for successful implementation.

Sources

FDA PCCP Guidance · FDA
FDA - AI/ML-Enabled Medical Devices · FDA
IMDRF - Software as a Medical Device · IMDRF

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Why compare MedTech terms side by side?

MedTech terminology is full of pairs that look interchangeable but carry very different regulatory, clinical, and commercial consequences. Picking the wrong framework, pathway, or standard early in a project can add months to a submission, invalidate clinical evidence, or trigger an audit finding. Side-by-side comparison is the fastest way to surface those differences before they become costly mistakes.

Each comparison on this page pulls from the same vendor-neutral, sourced definitions used throughout the MedTech Terms glossary. You see the one-line summary, the formal definition, why it matters in practice, common pitfalls, and the primary sources (FDA guidance, EU MDR/IVDR articles, ISO/IEC standards, MDCG documents, IMDRF principles) that back each entry. That makes the comparison defensible in regulatory strategy memos, design reviews, and submission narratives.

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.
EU frameworks - MDR vs IVDR, CE Mark vs UKCA.
Cybersecurity artifacts - SPDX vs CycloneDX SBOM, CVE vs CVSS.
Post-market actions - Recall vs field action, Removal vs correction.

How to use this tool

Pick term A and term B from the dropdowns, or click a preset above. The URL updates with both slugs so you can bookmark or share the exact comparison with a colleague, a notified body reviewer, or your regulatory consultant. Click Open full page on either side for the complete entry, including FAQs, related terms, and the full citation list. If you are not sure which term to start with, browse the Categories view or the A-Z index.

MedTech Terms is a vendor-neutral community resource sponsored by Blue Goat Cyber. Definitions are written for educational use and are not legal or regulatory advice.