Artificial Intelligence and Machine Learning for Biomedical Diagnostics and Prognostics

Dear Colleagues,

This Special Issue invites original research and reviews on the design, development, and validation of Artificial Intelligence (AI) and Machine Learning (ML) methods applied to detection, diagnosis, risk stratification, and prediction of clinical outcomes. We welcome a broad range of study designs, including individually randomized, cluster‐randomized, and stepped-wedge trials; pragmatic and adaptive trials; cohort, case–control, cross-sectional, and longitudinal studies; N-of-1 trials and interrupted time-series analyses; diagnostic and prognostic accuracy studies; clinical impact and decision-analytic evaluations; as well as systematic reviews and meta-analyses.

Submissions are encouraged across psychology and psychiatry (e.g., ecological momentary assessment, relapse prediction, suicide risk modeling, treatment response to psychotherapies and pharmacotherapies, neuroimaging, and speech/language analytics); physiotherapy and rehabilitation (e.g., telerehabilitation trials, gait analysis using inertial sensors, and exercise protocols); and nutrition (e.g., controlled dietary interventions, crossover feeding studies, nutrigenomics, and metabolomics for personalized nutrition). Additional medical domains of interest include cardiology (ECG and wearable sensors), oncology (prognosis and treatment response), sleep medicine, critical care, pediatrics, and geriatrics.

We particularly value multimodal data integration (medical imaging, omics, digital pathology, wearable sensors, and electronic health records), supervised and unsupervised learning, transfer learning, generative models, and explainable/interpretable approaches. Submissions should demonstrate clinical utility, robust calibration, multicenter generalizability, fairness, privacy and safety safeguards, and—where possible—open datasets, reproducible code, external validation, comparison to clinical standards, and prospective evaluation to accelerate the translation of AI into measurable health impact.

Dr. César Byron Guevara Maldonado
Dr. Victoria López
Dr. Diego Fernando Riofrío Luzcando
Guest Editors

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