AI Model Transparency: The Role of Explainable AI (XAI) in Regulatory Approval

AI models in healthcare must not only be accurate but also explainable. Regulators like the FDA, EMA, and NMPA require Explainable AI (XAI) to ensure that AI-driven decisions are understandable, interpretable, and justifiable—especially for high-risk applications like diagnostics.

Why Regulators Demand Explainable AI (XAI)

Regulators classify AI-based medical devices under Software as a Medical Device (SaMD), meaning they directly influence clinical decisions and patient outcomes. Black-box AI models, which provide results without transparency into their decision-making process, pose risks such as:

• Bias in Predictions: If the AI system is trained on non-representative datasets, it may provide inaccurate results for certain demographics.
• Lack of Clinical Trust: Physicians and healthcare providers must understand how the AI reaches its conclusions to confidently use it in diagnosis and treatment.
• Regulatory Rejection: AI models that cannot demonstrate traceability and reproducibility may be denied approval by regulators like the FDA.

Methods to Achieve AI Model Transparency

To meet regulatory requirements, AI developers must integrate XAI techniques that improve interpretability. Some common methods include:

1. Feature Attribution Models

• SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) highlight which features (e.g., ECG waveform, CT scan pixels) influence the AI’s decision.
• Regulators prefer these techniques for risk stratification models and imaging AI.

2. Rule-Based and Decision Tree Models

• Decision trees and symbolic AI approaches allow for transparent decision-making while maintaining high accuracy.
• Hybrid AI systems, combining deep learning with knowledge-based rules, are more acceptable for regulatory approval.

3. Model Validation with Clinicians

• AI models must undergo clinical validation where doctors verify AI decisions against real patient data.
• Post-market monitoring is essential to track AI performance over time.

Regulatory Guidelines on AI Transparency

Different regulatory bodies have issued guidance on AI explainability:

• FDA’s Good Machine Learning Practices (GMLP): Recommends AI models be reproducible, interpretable, and continuously monitored.
• EU MDR & CE Mark Requirements: AI software must comply with ISO 14971 (Risk Management) and ISO 13485 (QMS), ensuring human oversight in AI-driven decisions.
• IMDRF SaMD Guidelines: AI models should include explainability reports as part of their regulatory submission.

Challenges in AI Transparency & Compliance

Despite advances in XAI, challenges remain:

• Deep learning models (CNNs, RNNs) remain complex, making full interpretability difficult.
• Trade-off between accuracy and explainability—simpler models may be more transparent but less powerful.
• Lack of standardized AI transparency metrics across regulatory bodies.

To overcome these, AI developers should focus on hybrid approaches, clinical validation, and continuous monitoring to align with regulatory expectations.

Conclusion

Explainable AI (XAI) is now a regulatory requirement for AI-driven medical devices. Developers must integrate feature attribution models, human oversight, and clinical validation to ensure regulatory approval. At ITR VN, we help MedTech companies build regulatory-compliant AI systems, ensuring transparency and approval success.

Need help with AI model transparency? Contact us today!

ITR – A trusted tech hub in MedTech and Digital Health

‍

‍