Call for Papers for Special Issue: Intelligent Medicine: Machine Learning and Explainable AI for Next-Generation Healthcare

Homepage SI: https://link.springer.com/collections/gjbedjebba

Topical Collection in Springer Nature Discover Computing (Open Access) — Open for submissions

The healthcare sector is rapidly transforming through Machine Learning (ML) and Artificial Intelligence (AI), increasingly supporting diagnosis, prognosis, and clinical decision-making. At the same time, real-world deployment requires more than accuracy: models must be interpretable, fair, robust, and trustworthy. This Topical Collection invites high-quality contributions advancing ML theory, methods, and applications tailored to clinical, epidemiological, and public-health settings, with a strong emphasis on explainability as a transparency requirement and as practical support for clinicians and learners.

We particularly encourage submissions that integrate structured electronic health records with imaging, signals, and clinical text; address uncertainty and fairness; and enable privacy-preserving, regulation-compliant collaboration across institutions.

Topics of Interest

• Predictive Modeling for Diagnosis and Prognosis: Advanced ML architectures for risk stratification, early detection, treatment-response prediction, postoperative outcome modeling, and survival analysis.

• Comorbidity Analysis and Longitudinal Patient Trajectories: Representation learning and temporal modeling for disease interactions, multimorbidity networks, state-transition modeling, and dynamic patient phenotyping based on multivariate or multimodal time-series data.

• Multimodal Data Integration: Techniques merging structured EHRs with imaging (MRI, CT, X-ray), physiological signals (ECG, EEG, wearable data), genomics, and clinical narratives through attention mechanisms, graph-based learning, transformers, and foundation-model adaptation.

• Federated, Distributed, and Privacy-Preserving Learning: Federated optimization, secure aggregation, differential privacy, and decentralized architectures enabling cross-institutional collaboration while safeguarding patient confidentiality and ensuring regulatory compliance.

• Fairness, Causality, Robustness, and Trustworthy ML: Approaches addressing algorithmic bias, causal inference and counterfactual reasoning, calibration and uncertainty quantification, out-of-distribution robustness, and explainability techniques designed for clinical auditability.

• Ethical, Educational, and Human-Centered AI: Interpretable ML systems that enhance clinical training, support explainable decision pathways, improve AI literacy, and facilitate responsible deployment of AI-enabled healthcare tools.

• Human–Robot Interaction and Intelligent Interfaces in Healthcare: Adaptive clinical interfaces, affective computing for patient engagement, assistive robotics, and cognitive-support systems for medical staff and learners.

We warmly welcome submissions that advance explainable and trustworthy AI in healthcare, with a focus on methodological innovation and clinically relevant applications. To keep the Collection aligned with this focus, studies primarily centered on sentiment analysis or opinion mining of AI adoption fall outside the intended scope.

Submission information

• Venue: Springer Nature – Discover Computing (Open Access).
• Submission status: Open for submissions.
• Deadline: October, 5 – 2026
• Submissions to collections are made through the participating journal by the stated deadline and undergo the journal’s standard editorial and peer-review processes.

Collection Editors

• Enrico Barbierato, Catholic University of the Sacred Heart, Brescia, Italy
• Manuel Striani, University of Eastern Piedmont (UPO/DiSIT), Alessandria, Italy

Keywords: Machine Learning; Explainable AI; Healthcare; Comorbidity; Multimodal Learning; Time Series; Federated Learning; Causal Inference; Trustworthy AI; Medical Education; HCI for Personal Healthcare Assistant