AI/ML device tool

PCCP Builder

Draft an 8-section SDS-PCCP outline aligned to FDA's December 2024 PCCP guidance - with per-modification cybersecurity impact and the deficiency flags reviewers actually cite.

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8-section SDS-PCCP outline aligned to FDA December 2024 guidance

• Per-modification FDA disposition tag (PCCP-eligible, borderline, or out-of-scope) - so you know upfront which items reviewers will push back on.
• Per-modification cybersecurity impact and the named deficiency-letter language reviewers use.
• Reviewer-rejection flag list computed from your inputs - fix these before you submit.
• Markdown export of the full 8-section outline you can hand to regulatory + ML teams as the SDS-PCCP draft.

What teams usually get wrong

• Myth: PCCP is only for continuously-learning AI.

  Reality: FDA's guidance applies PCCP to any planned change to a software function - including locked algorithms and deterministic updates. 'Predetermined' is about the plan, not the model class.

• Myth: PCCP lets us push any model update without a new submission.

  Reality: Only changes inside the documented modification scope are exempt. Hardware expansion, new intended use, new interfaces, and most architecture swaps still require a new 510(k) or supplement.

• Myth: We can skip cybersecurity in the PCCP if the device already has §524B docs.

  Reality: The FDA's Feb 3, 2026 final cyber guidance requires explicit cybersecurity impact analysis for every modification path (SBOM diff, threat model delta, attack surface review), separate from the baseline §524B package.

• Myth: Rollback is a deployment concern, not a regulatory one.

  Reality: FDA expects (a) a documented rollback trigger, (b) evidence the rollback is exercised before each release, and (c) a notification plan to clinicians. Documentation alone is rejected.

• Myth: Real-world performance monitoring is optional if we have a strong test set.

  Reality: RWPM is the most common PCCP deficiency. For any model that retrains or adapts, it's effectively mandatory - and reviewers want named metrics, thresholds, and cadence.

Primary sources behind this tool

1. Marketing Submission Recommendations for a Predetermined Change Control Plan (Dec 2024) - FDA
2. Good Machine Learning Practice for Medical Device Development - Guiding Principles - FDA / Health Canada / MHRA
3. Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions (Feb 3, 2026 final) - FDA
4. Transparency for Machine Learning-Enabled Medical Devices - Guiding Principles - FDA
5. Artificial Intelligence and Machine Learning in Software as a Medical Device - Action Plan - FDA

Go deeper

Take it from outline to submission.