Lack of Standardized AI Transparency Metrics Across Regulatory Bodies

One of the biggest challenges in AI regulatory approval is the lack of standardized transparency metrics across different regions. While agencies like the FDA (U.S.), EMA (Europe), and NMPA (China) all require Explainable AI (XAI) for high-risk medical applications, they do not yet have unified criteria to measure AI transparency and interpretability.

Inconsistencies Across Regulatory Bodies

• The FDA’s Good Machine Learning Practices (GMLP) emphasize reproducibility and traceability, but there is no clear benchmark for how much explainability is "enough."
• The EU MDR and CE marking process require AI to be auditable, but ISO 13485 and ISO 14971 do not specify how AI explanations should be structured.
• IMDRF (International Medical Device Regulators Forum) provides SaMD guidelines, but each country interprets these differently.

Challenges Due to the Lack of Standards

• Difficulties in Compliance: AI developers struggle to design models that satisfy multiple agencies without a unified framework.
• Trade-off Between Accuracy and Interpretability: Some regulators favor decision trees or rule-based systems, while others accept deep learning models with post-hoc explainability methods (e.g., SHAP, LIME).
• Post-Market Surveillance Issues: Without standard transparency metrics, tracking AI drift and model bias over time is inconsistent.

Potential Solutions and Industry Trends

• Developing AI-Specific Regulatory Standards: Agencies like the FDA and EMA are moving toward clearer AI explainability metrics. The FDA's AI/ML-Based SaMD Action Plan suggests adaptive algorithms will require ongoing validation.
• Adopting Common XAI Methods: Techniques like SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and Feature Importance Mapping are becoming widely accepted.
• Harmonization of Global Standards: The industry is pushing for alignment between ISO, FDA, and IMDRF guidelines, particularly through initiatives like ISO/IEC 42001 (AI Management Systems Standard).

Conclusion

AI transparency is critical for regulatory approval, but the lack of standardized metrics creates uncertainty for MedTech companies. To mitigate risks, AI developers should follow best practices in interpretability, validation, and risk management while keeping track of emerging global AI standards.

At ITR, we help companies design AI models that align with current and future regulatory expectations—ensuring explainability, compliance, and approval success.

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