Interpretable AI and Electronic System for Digital Health Biomarkers: From Sensing to Monitoring

Dear Colleagues,

This Special Issue calls for advances that make AI-driven digital biomarkers for health monitoring transparent, interpretable, trustworthy, and clinically actionable across the clinic–home continuum. We seek contributions that turn raw clinical data into interpretable evidence at every step, from data curation and harmonization to model development, explanation, and prospective evaluation in real workflows. Target applications include early detection and risk stratification, differential diagnosis, disease staging and progression modeling, patient-specific prognosis, and monitoring of treatment response. Modalities span structural and molecular imaging (MRI/PET), biosignals (EEG/ECG/EMG/GSR), speech and handwriting, gait and kinematics, ocular/retinal measures, and multimodal wearables/smartphones digital biomarkers, including passive sensing. Submissions should prioritize interpretable reasoning (concept-based, prototype, counterfactual, causal), calibrated uncertainty, fairness and bias assessment, robustness to domain shift, and reporting that enables reproducibility and external validation. We particularly welcome multi-site studies, federated or privacy-preserving learning, human-in-the-loop AI-methodologies, and real-world deployments that demonstrate clinical utility. Reviews, benchmarks, negative/neutral results with rigorous error analysis, and open datasets/tools that accelerate translation are also encouraged.

Dr. Gianluca Amprimo
Dr. Claudia Ferraris
Dr. Andreas Miltiadous
Guest Editors

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