Computational Models and Machine Learning for Biomedical Applications

Dear Colleagues,

This Special Issue aims to bring together cutting-edge research on computational models and machine learning methods that address key challenges in contemporary biomedicine and healthcare. We invite contributions that develop novel algorithms, robust computational frameworks, and clinically relevant applications for medical screening, diagnostic support, risk prediction, patient monitoring, and therapeutic decision-making.

We are particularly interested in studies that leverage recent advances in deep learning and foundation models, including large-scale and multimodal architectures that integrate imaging, biosignals, omics data, electronic health records, and clinical text. Topics of interest include, but are not limited to, medical image and signal analysis; computational pathology; genomics and other high-throughput omics; digital and personalized medicine; and AI-driven drug discovery and therapy optimization.

We also welcome contributions on multimodal and explainable AI, model robustness, uncertainty quantification, bias and fairness, privacy-preserving and federated learning, and methods designed for low-resource or real-time clinical settings. Studies connecting methodological innovation with experimental validation, clinical evaluation, open datasets, or reproducible software tools are strongly encouraged. This Special Issue aims to highlight reliable, transparent, and sustainable computational solutions that can advance biomedical research and improve healthcare outcomes.

Prof. Dr. Mariusz Pelc
Dr. Aleksandra Kawala-Sterniuk
Guest Editors

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