Biomedical Signal and Image Processing with Artificial Intelligence

Dear Colleagues,

This Special Issue aims to highlight the transformative impact of advanced AI technologies, including Large Language Models (LLMs), generative AI, deep learning, and machine learning, on the field of biomedical engineering. As AI continues to evolve, its applications in healthcare are becoming increasingly sophisticated, enabling groundbreaking advancements in the acquisition, processing, analysis, and interpretation of biomedical signals and images. This Special Issue will focus on the latest research integrating state-of-the-art AI methodologies to address critical challenges in healthcare, such as early disease detection, personalized treatment, and automated diagnostics.

Recent advancements in generative AI, including models like Generative Adversarial Networks (GANs) and diffusion models, have opened new possibilities for synthetic data generation, image reconstruction, and augmentation in medical imaging. These technologies are particularly valuable in scenarios where labeled data are scarce, enabling the creation of high-quality synthetic datasets for training robust AI models. Similarly, Large Language Models (LLMs), such as GPT and BERT, are being leveraged to process and interpret unstructured clinical text, enabling the seamless integration of multimodal data (e.g., combining imaging, signals, and electronic health records) for comprehensive patient analysis.

This Special Issue will also explore the role of foundation models and self-supervised learning in biomedical applications, which have shown remarkable success in reducing the dependency on large, annotated datasets. Additionally, the integration of edge AI and real-time processing techniques is revolutionizing point-of-care diagnostics, enabling faster and more efficient decision-making in clinical settings. Topics of interest include but are not limited to the following:

• AI-driven diagnostic tools for early disease detection and prognosis;
• Automated segmentation and classification of medical images using deep learning;
• Generative AI for synthetic data generation and image reconstruction;
• Multimodal AI systems combining signals, images, and text for holistic patient analysis;
• Real-time signal processing for wearable devices and remote monitoring;
• Explainable AI (XAI) for transparent and interpretable healthcare solutions;
• LLMs for clinical text analysis, report generation, and decision support;
• Federated learning for privacy-preserving collaborative AI in healthcare;
• AI-powered personalized medicine and treatment optimization;
• Applications of reinforcement learning and transfer learning in biomedical signal and image processing.

This Special Issue aims to showcase the latest innovations and foster interdisciplinary collaboration. It will serve as a platform for disseminating cutting-edge research that bridges the gap between AI and biomedical engineering, ultimately advancing the development of intelligent, efficient, and accessible healthcare solutions.

Dr. Vivek Singh
Dr. Alessandra Lumini
Guest Editors

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