The Role of Artificial Intelligence Technologies in Health
A special issue of Applied Sciences (ISSN 2076-3417).
Deadline for manuscript submissions: 20 November 2025 | Viewed by 1100
Special Issue Editors
Interests: medical imaging; intelligent systems; bioengineering; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) has emerged as a transformative force in healthcare, offering novel approaches for analyzing complex biomedical data and supporting clinical decision-making. With the increasing availability of high-dimensional data, such as medical images, physiological signals, and multi-omics profiles, AI-driven techniques have become essential for extracting robust and reproducible biomarkers, detecting pathological patterns, and enhancing diagnostic accuracy.
This Special Issue focuses on recent advances in AI applications for biomedical image and signal analyses, with particular emphasis on model explainability and the emerging paradigm of digital twins. We invite submissions that explore supervised, unsupervised, and self-supervised methods for segmentation, classification, and prediction in clinical domains such as radiology, neurology, cardiology, and oncology.
Contributions addressing explainable AI (XAI) are especially encouraged, including interpretable deep learning architectures, attention mechanisms, and post hoc explanation techniques, aimed at improving trust in clinical settings.
Furthermore, we seek research on AI-enabled digital twins and virtual patient-specific models that integrate real-time physiological data and computational simulations to support personalized diagnostics, treatment planning, and disease progression monitoring in the paradigm of precision medicine.
By bridging algorithmic innovation with translational medicine, this Special Issue aims to highlight interdisciplinary research that could enable the adoption of AI technologies in real-world healthcare scenarios.
We welcome original research, methodological innovations, and comprehensive reviews on state-of-the-art approaches.
Dr. Antonio Brunetti
Guest Editor
Dr. Elena Sibilano
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- deep learning for medical imaging
- multimodal biomedical signal analysis
- AI for radiomics and radiogenomics
- explainable AI
- digital twin models
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