Machine learning evolves through retraining, dataset expansion, and hyperparameter tuning. Traditional regulations expect static, frozen code. PMX OS resolves this fundamental conflict by operationalizing FDA Predetermined Change Control Plans (PCCP), model drift surveillance, and complete dataset lineage.
Medical software is no longer a static binary compiled once and distributed on CD-ROM. It is an evolving cognitive pipeline.
When deployed across diverse clinical cohorts, physiological feature distributions shift. Without automated telemetry monitoring tied to risk files, subtle sensitivity loss remains undetected until an adverse event occurs.
Auditors now ask: "Show the exact patient raw waveform subset that produced model weights hash 0x4f82." Traditional eQMS stores PDFs of validation summaries, not immutable training data lineage.
Teams must simultaneously comply with FDA 510(k)/De Novo requirements, EU MDR Class IIa/IIb clinical evaluation rules, and the EU AI Act’s stringent Annex IV technical documentation and cybersecurity duties.
Under FDA guidance, a pre-cleared PCCP permits specific planned algorithmic modifications without submitting a new 510(k). PMX OS enforces the three statutory PCCP pillars mathematically in your knowledge graph.
Explicitly bounds what parameters may change: model architecture tweaks, hyperparameter bounds, and input preprocessing filters. PMX OS flags any commit attempting changes outside these authorized graph constraints.
Defines verification protocols, ground-truth annotation rules, and clinical test datasets. PMX OS automatically verifies that retraining runs satisfy statutory acceptance thresholds (e.g. AUROC ≥ 0.96, specificity ≥ 98%).
Links the proposed modification directly to ISO 14971 risk nodes. Confirms that algorithmic shifts introduce no new hazards and do not alter the primary Indications for Use (IFU).
Executing an algorithm upgrade (v2.4 to v2.5) safely within authorized PCCP boundaries.
The data science team at CardioSense Pro developed an updated 1D convolutional neural network (ALG-ECG-02 v2.5) trained on 14,000 newly annotated Holter monitor segments to reduce false positives in ambulatory pediatric patients.
In a traditional device company, submitting this change would trigger an immediate executive debate: "Does this require a full Special 510(k) or can we Letter-to-File?" Months of paralysis typically ensue.
PMX OS evaluated the model PR directly against pre-cleared PCCP Protocol 3 in clearance K221840:
The system auto-generated an immutable verification dossier and logged it to the active DHF.
Software as a Medical Device classified under MDR Class IIa/IIb automatically falls under High-Risk AI System rules under Annex I of the EU AI Act.
| EU AI Act Statutory Duty | Operational Burden | PMX OS Automated Solution |
|---|---|---|
| Risk Management System (Article 9) | Continuous risk management across entire AI lifecycle | Bidirectional semantic linkage between training anomalies and ISO 14971 risk files |
| Data & Data Governance (Article 10) | Data provenance, bias mitigation, and demographic stratification | Cryptographic dataset hashing, patient cohort breakdown charts, and lineage graphs |
| Technical Documentation (Article 11) | Comprehensive system architecture and training parameters | Auto-compiled Annex IV technical file ready for Notified Body conformity inspection |
| Human Oversight (Article 14) | Mechanisms to prevent automation bias and clinical override controls | Documented clinician confirmation nodes linked directly to SaMD GUI state machines |
Talk with our medical AI regulatory engineers to see how PMX OS handles PCCP authoring, model versioning, and automated dataset audits.