Machine Learning for Advanced Healthcare: Bridging Innovation and Clinical Implementation

Dear Colleagues,

Digital transformation is revolutionizing healthcare, leading to a more efficient management of health-related information, diagnostics, and patient care. Machine learning (ML) plays a crucial role in this evolution, offering advanced solutions for early disease detection, continuous patient monitoring, and personalized treatment. However, the efficacious translation of ML from proof-of-concept studies to real-world clinical applications not only requires high predictive accuracy, but also transparent, reliable, and interpretable decision-making processes.

The growing availability of multimodal health data—from physiological signals, wearable sensors, medical imaging, and remote or real-time patient monitoring—offers the opportunity to develop explainable and reliable ML frameworks. Hence, these strategies could significantly support clinical decision-making, particularly benefiting individuals with chronic conditions, ageing populations, and those with limited healthcare access.

This Special Issue aims to bridge the gap between ML advancements and their actual implementation in healthcare; specifically, it focuses on the development and application of interpretable ML frameworks in real-world clinical settings.

For this Special Issue, we invite submissions presenting explainable AI strategies, multi-source data integration, and interpretable solutions to support early diagnosis, monitoring, and predictive modelling of disease progression. Studies addressing challenges in data integration, model interpretability, physician–AI interaction, and privacy-preserving approaches are highly encouraged.

Potential topics include, but are not limited to:

• ML models to support early diagnosis and disease progression monitoring;
• Wearable technologies and sensor-based data analysis;
• Interpretable methods for sleep, motor, and cognitive assessments;
• Multimodal data integration and fusion;
• Explainable AI for clinical decision-making;
• ML for remote monitoring and telemedicine;
• Predictive models for personalized treatment;
• Federated learning and privacy-preserving ML;
• Big data in healthcare and e-Health;
• Generalizability, interpretability, and visualization in healthcare.

Dr. Irene Rechichi
Dr. Silvia Seoni
Dr. Sujit Kumar Sahu
Guest Editors

Manuscript Submission Information

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