Clinical Applications of Artificial Intelligence and Machine Learning in Diagnosis and Therapy

Dear Colleagues,

In recent years, artificial intelligence (AI) and machine learning (ML) have profoundly transformed biomedical research and clinical practice. Advances in computational methods, the growing availability of multimodal biomedical data, and the rapid development of deep learning architectures have enabled new approaches to diagnosis, prognosis, and treatment personalization across many areas of medicine. From medical imaging analysis to digital biomarkers and predictive modeling, AI-driven tools are increasingly being integrated into healthcare systems, supporting clinicians in making more accurate, timely, and personalized decisions.

At the same time, several challenges remain. The clinical translation of AI systems requires robust validation, interpretability, reproducibility, and compliance with emerging regulatory frameworks. Moreover, the complexity of biomedical data demands interdisciplinary methodologies capable of extracting meaningful patterns and generating clinically actionable knowledge.

This Special Issue aims to present and disseminate the most recent advances in the application of AI and ML to clinical diagnosis, prognosis, and therapeutic decision-making. We welcome contributions addressing real-world clinical applications of AI-driven systems across neurological, psychiatric, and broader medical domains.

Topics of interest for publication include, but are not limited to, the following:

• Artificial intelligence in clinical diagnosis systems;
• Machine learning methods for neuroimaging and medical imaging analysis;
• Digital biomarkers and computational phenotyping of multifactorial diseases;
• Explainable and interpretable AI for clinical applications;
• Multimodal data integration (e.g., imaging, clinical, genomic, and behavioral data);
• AI-driven predictive models for disease progression and treatment response;
• Machine learning approaches for cognitive and behavioral data analysis;
• Deep learning and foundation models for biomedical data;
• AI-based tools for personalized and precision medicine;
• Human–AI interaction and patient-centered design of clinical AI systems.

This Special Issue seeks to foster interdisciplinary collaboration among clinicians, neuroscientists, computer scientists, and data scientists, with the goal of advancing the responsible and effective integration of artificial intelligence into clinical practice.

Dr. Christian Salvatore
Dr. Valentina Chiappa
Dr. Gennaro D’Anna
Guest Editors

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• artificial intelligence in clinical medicine
• machine learning in clinical medicine
• medical imaging analysis
• digital biomarkers
• explainable artificial intelligence (XAI)
• precision medicine
• human–AI interaction in healthcare