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: FDA's Sept 2023 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 (Sept 2023) - 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.