Artificial intelligence (AI) has emerged as a powerful tool in various fields, including medical signal and image processing. Integrating AI techniques into medical imaging and signal analysis has revolutionized how healthcare professionals diagnose and treat diseases. Medical signal and image processing involve the analysis and interpretation of various types of signals and images generated by imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, electrocardiogram (ECG), electroencephalogram (EEG), and electromyography (EMG). This collection explores the latest advancements, challenges, and potential applications of AI techniques in the analysis and interpretation of medical signals and images. 

This collection seeks to cover a broad range of topics related to the integration of artificial intelligence in medical signal and image processing, including but not limited to: 

• Machine learning and deep learning algorithms for medical image and signal analysis 
• AI-based segmentation and registration techniques for medical images 
• Computer-aided diagnosis and decision support systems 
• Real-time and edge computing for medical signal and image analysis 
• AI-based techniques for medical signal denoising, enhancement, and feature extraction 
• AI-driven approaches for disease detection, classification, and prognosis 
• Integrating AI algorithms with medical imaging modalities (e.g., MRI, CT, and ultrasound) 
• Privacy and security challenges in AI-enabled medical signal and image processing