Explainable AI in Medical Device Signal Processing: A Validation Framework for Transparent AI in Healthcare
Signal processing in medical devices increasingly relies on machine-learning models, and manufacturers are adopting XAI techniques to improve transparency and interpretability of these models. The data generated by modern devices like ECGs, EEGs, and wearable biosensors is quite large and, hence, the use of advanced machine-learning algorithms is increasingly common for extracting clinically relevant information. Although AI significantly deepens diagnostic insights, the black-box models’ obscurity still curtails clinical trust and, at the same time, raises regulatory concerns related to transparency, traceability, and risk control.
The paper, presented here, explains how the use of interpretability techniques like SHapley Additive exPlanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME) will help in building trust in signal processing models, and to this end, a validation framework has been put forward that marries explainability with classical statistical evaluation.
Interpretability in Signal Processing using XAI
LIME and SHAP are widely used XAI techniques for explaining model behavior in healthcare AI. While LIME simplifies the decision-making process by creating easy to understand surrogate models for individual predictions, SHAP, on the other hand, employs Shapley values as a tool to measure the overall and local effect of particular input features on the model.
Working with time-series data poses unique challenges compared to imaging or static datasets. Device signals often contain noise, multi channel variability and temporal dependencies that require careful preprocessing. SHAP can highlight which engineered features or time-windowed signal components contribute most to a model’s prediction. LIME approximates local model behavior by perturbing inputs and fitting an interpretable surrogate model around individual predictions.
When used together, these methods provide complementary perspectives on model behavior, strengthening interpretability assessments.
Validation Framework integrating XAI and Statistical Methods
Traditional statistical validation using sensitivity, specificity, ROC curves and Bland Altman analyses remains essential for evaluating signal processing performance. For time-series signals, validation may also include temporal consistency checks, segment-level performance, and robustness to noise and artifacts. However, machine-learning algorithms applied to device signals need an additional interpretability dimension.
A combined validation framework can include the following steps:
⦿ Signal feature engineering and baseline benchmarking using conventional algorithms.
⦿ Model training with AI and machine-learning algorithms, including deep learning and ensemble signal classifiers for time-series data.
⦿ Interpretability integration using SHAP for global or local feature attribution and LIME for instance level explanations.
⦿ Interpretability assessment by comparing SHAP and LIME explanations against known physiological and signal-processing expectations with physiological expectations for example, physiologically relevant features such as QRS-related components in ECG arrhythmia detection tasks.
⦿ Statistical performance evaluation using cross validation, external validation and drift analyses to ensure performance stability in real world settings.
⦿ Regulatory alignment and lifecycle monitoring where XAI outputs are used to support risk analysis, traceability, and change impact assessment in alignment with standards such as ISO 14971.
This blended model adds an interpretability validation loop that strengthens transparency and supports regulatory submissions, clinical adoption and scholarly publication.
Why this matters for regulatory consulting
For medical device manufacturers and SaMD developers, explainability is increasingly expected or strongly encouraged for AI-enabled signal analysis in regulated medical devices. Global regulators emphasize transparency, traceability and ongoing monitoring of adaptive machine-learning algorithms.
By combining interpretability tools with classical validation, stakeholders can show that their systems are robust, trustworthy and compliant. This transforms black-box models into auditable frameworks well aligned with the expectations of peer-reviewed journals such as Diagnostics, Sensors, Frontiers in AI and IEEE JBHI.
Conclusion
As AI continues to shape medical device signal processing, a structured validation framework that blends XAI, time-series data interpretation and traditional statistical verification becomes essential. This supports regulatory preparedness, enhances clinical trust and ensures scientific rigor.
At Elexes, we specialise in regulatory consulting for medical devices and AI ML systems. Contact us if you need support integrating explainability into your device development lifecycle.
