Understanding IMDRF's Final Guidance: Implications for Medical Device Developers

The International Medical Device Regulators Forum (IMDRF) has released the final versions of two key guidance documents that will significantly shape the development and regulatory landscape for medical device software, including AI-powered medical devices. These documents are:

• IMDRF/SaMD WG/N81 FINAL: 2025 - Characterization Considerations for Medical Device Software and Software-Specific Risk
• IMDRF/AIML WG/N88 FINAL: 2025 - Good Machine Learning Practice for Medical Device Development: Guiding Principles

These finalized documents provide harmonized regulatory expectations to ensure safety, effectiveness, and transparency in medical device software and AI-driven technologies.

Key Updates and Changes from Draft to Final Version

IMDRF/SaMD WG/N81 FINAL: 2025 - Medical Device Software Risk Characterization

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1. Broader Scope for Risk Characterization
   • The final version expands on risk characterization, emphasizing device classification frameworks and a structured approach for risk management.
   • The role of software within medical workflows is now a critical consideration in defining risk categories.
2. Refined Device Characterization Guidelines
   • Improved guidelines on intended use statements, which now include structured requirements for medical purpose definitions.
   • The document includes new appendices with characterization feature summaries and real-world examples linking characterization features to risk assessment.
3. Enhanced Considerations for Software Hazards
   • Additional insights on software-specific hazards related to device interoperability, cybersecurity, and user interface design.
   • New emphasis on how information-related hazards can contribute to risk, particularly for AI-powered decision support tools.
4. Risk Categorization Improvements
   • The final version introduces more structured risk categorization methodologies, ensuring clearer alignment with jurisdiction-specific classification frameworks.

IMDRF/AIML WG/N88 FINAL: 2025 - Good Machine Learning Practice (GMLP) for Medical Devices

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1. Expanded Considerations for Generative AI
   • New sections address Generative AI's role in medical device development, focusing on the challenges of foundation models and Software of Unknown Provenance (SOUP).
   • Stronger regulatory focus on ensuring traceability and reproducibility of AI-generated outputs.
2. Emphasis on Lifecycle Monitoring & Post-Market Surveillance
   • Enhanced guidance on continuous performance monitoring for deployed AI models.
   • Introduces best practices for AI model updates, including controls to mitigate risks of bias, overfitting, or unintended performance degradation.
3. Updated Requirements for Data Representation & Bias Mitigation
   • New mandates to ensure training datasets represent diverse patient populations.
   • Strengthened focus on real-world performance validation, addressing the risk of dataset drift over time.
4. Improved Transparency & Explainability Requirements
   • Greater clarity on AI explainability standards, ensuring healthcare providers can interpret AI-driven outputs.
   • New best practices for human-AI interaction assessment within clinical workflows.

What These Changes Mean for Medical Device Developers

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Regulatory Compliance & Documentation

• Developers should align their risk management frameworks with IMDRF’s expanded categorization criteria.
• AI-driven device manufacturers must document explainability and data provenance in greater detail.

Design & Development Adjustments

• More robust risk mitigation strategies will be required for AI-based diagnostic tools and autonomous medical decision-making software.
• Developers must establish post-market surveillance protocols for tracking real-world AI performance.

Market Readiness & Competitive Advantage

• Compliance with these guidelines will streamline regulatory approvals in multiple jurisdictions.
• Companies that proactively adopt GMLP principles will gain a competitive edge by ensuring safer and more reliable AI-powered medical devices.

Conclusion

The finalized IMDRF guidance represents a significant step toward global regulatory harmonization for medical device software and AI-based healthcare solutions. These documents provide clear expectations for risk management, AI transparency, and performance monitoring—critical areas for the future of digital health technologies.

By proactively incorporating these principles, medical device developers can accelerate regulatory approvals, enhance product safety, and foster greater trust in AI-powered healthcare solutions.

Next Steps:

• Review and update internal compliance strategies in line with the final IMDRF guidelines.
• Strengthen AI model validation protocols, ensuring compliance with new transparency and bias mitigation requirements.
• Stay engaged with regulatory bodies to anticipate future updates in global AI and medical device software standards.

With these final guidelines now in place, the industry is better equipped to develop innovative, safe, and effective medical device software solutions for the global healthcare market.

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