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Software & AI [AI / ML in Devices](/ecosystems/ai-ml)[Software Lifecycle](/ecosystems/software-lifecycle)ACP 

# Algorithm Change Protocol

The detailed procedural section of a PCCP that specifies how planned modifications to an AI/ML model will be developed, validated, and implemented.

Reviewed by [Christian Espinosa, Founder, Blue Goat Cyber](/authors/christian-espinosa) Last reviewed June 20, 2026 

## Definition

An Algorithm Change Protocol (ACP) is one of three required components of a  [Predetermined Change Control Plan](/terms/ai-ml-pccp) (PCCP) for AI/ML-enabled device software under FDA's 2024 final guidance. The ACP specifies the methodology for implementing the planned modifications described in the Description of Modifications: data management protocols, retraining triggers, performance evaluation methods, statistical analysis plans, acceptance criteria, update implementation procedures, transparency to users, and traceability. A well-constructed ACP is what lets manufacturers iterate on AI/ML models within an approved PCCP without filing supplements for each retrain. 

What the regulation says

FDA's 2024 'Predetermined  [Change Control](/terms/change-control) Plans for Machine Learning-Enabled Device Software Functions' guidance specifies ACP content expectations. The Description of Modifications, ACP, and Impact Assessment together constitute the  [PCCP](/terms/ai-ml-pccp). 

## What this means in practice

The ACP is the section of a  [PCCP](/terms/ai-ml-pccp) where most of the technical and operational rigor lives. FDA reviewers focus on whether the ACP makes future changes predictable, evaluable, and traceable. Common ACP weaknesses include vague triggers ('when performance drifts'), missing acceptance criteria, no plan for handling subgroup performance degradation, and no specification of how user communication will be handled. A strong ACP often runs 30+ pages and is the centerpiece of the PCCP review. 

Common pitfalls

-   • Treating the ACP as a high-level outline, FDA expects specific protocols with quantitative criteria. 
-   • Omitting subgroup analysis methodology, disparate performance changes across patient subgroups are a focus area. 
-   • Failing to specify rollback procedures, what happens when an update doesn't meet acceptance criteria in production? 

## Related terms

Grouped by theme 

### Editor's picks

· Hand-selected related concepts 

[

Software & AI

AI/ML-Enabled Medical Device

Medical device that uses artificial intelligence or machine learning to perform its intended use.





](/terms/ai-ml-device)[

Software & AI

Explainability and Interpretability

Methods (SHAP, saliency maps, prototype explanations) that help users understand why a model made a given prediction.





](/terms/explainability)[

Software & AI

Good Machine Learning Practice(GMLP) 

Guiding principles for the development of AI/ML-enabled medical devices.





](/terms/good-machine-learning-practice)[

Software & AI

Locked vs. Adaptive Algorithm

Distinction between models whose behavior is fixed at release vs. those that continue learning.





](/terms/locked-vs-adaptive)

### More in Software & AI

· Same category 

[

Software & AI

Model Card

Structured documentation of a machine learning model's intended use, performance, and limitations.





](/terms/model-card)[

Software & AI

Model Drift Monitoring

Ongoing surveillance of input data and model outputs to detect performance degradation post-deployment.





](/terms/drift-monitoring)[

Software & AI

Predetermined Change Control Plan(PCCP) 

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





](/terms/ai-ml-pccp)[

Software & AI

AAMI TIR45(TIR45) 

AAMI Technical Information Report providing guidance on applying Agile software development practices within an IEC 62304-compliant medical device software lifecycle.





](/terms/aami-tir45)

Cited by

Where this term appears across MedTech Terms.

Ecosystems (2)

-   [AI / ML in Devices](/ecosystems/ai-ml)
-   [Software Lifecycle](/ecosystems/software-lifecycle)

## Primary references

3 sources 

Link health:  3 verified · last checked 2026-06-20 

FDA· 2 MDCG· 1 

1.  [1 
    
    PCCP Guidance for ML-Enabled Devices (Final 2024)
    
    Verified 
    
    FDA · fda.gov 
    
    
    
    ](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/predetermined-change-control-plans-machine-learning-enabled-device-software-functions)
2.  [2 
    
    MDCG Software Guidance
    
    Verified 
    
    MDCG · health.ec.europa.eu 
    
    
    
    ](https://health.ec.europa.eu/medical-devices-sector/new-regulations/guidance-mdcg-endorsed-documents-and-other-guidance_en)
3.  [3 
    
    FDA - Software as a Medical Device (SaMD)
    
    Verified 
    
    FDA · fda.gov 
    
    
    
    ](https://www.fda.gov/medical-devices/digital-health-center-excellence/software-medical-device-samd)

Inline markers like \[1\]  jump to the matching reference above.

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MedTech Terms is a community resource sponsored by [Blue Goat Cyber](https://bluegoatcyber.com). Definitions are independent of any vendor.

On this term

Category

Software & AI

Acronym

ACP

Sources

3

Updated

6/20/2026

[Compare with another term](/compare?a=algorithm-change-protocol)

Learn in 60 seconds

Card Lesson Quiz

The detailed procedural section of a PCCP that specifies how planned modifications to an AI/ML model will be developed, validated, and implemented.

-   · The ACP is the section of a PCCP where most of the technical and operational rigor lives. 
-   · FDA reviewers focus on whether the ACP makes future changes predictable, evaluable, and traceable. 
-   · A well-constructed ACP is what lets manufacturers iterate on AI/ML models within an approved PCCP without filing supplements for each retrain. 

Remember this

Watch out: Treating the ACP as a high-level outline, FDA expects specific protocols with quantitative criteria.

Related terms

-   [Predetermined Change Control Plan(PCCP) ](/terms/ai-ml-pccp)
-   [AI/ML-Enabled Medical Device ](/terms/ai-ml-device)
-   [Good Machine Learning Practice(GMLP) ](/terms/good-machine-learning-practice)
-   [Model Drift Monitoring ](/terms/drift-monitoring)
-   [Locked vs. Adaptive Algorithm ](/terms/locked-vs-adaptive)
-   [Model Card ](/terms/model-card)
-   [Explainability and Interpretability ](/terms/explainability)

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