Advanced AI and Data-Driven Learning Methods for Healthcare Applications

Dear Colleagues,

The healthcare industry is undergoing a transformation driven by the rapid advancements in artificial intelligence (AI), deep learning, machine learning, and data management. The integration of these technologies into healthcare applications holds great promise for improving medical diagnostics, treatment planning, and patient outcomes.

As healthcare data become increasingly complex and voluminous—comprising medical images (X-rays, MRIs, CT scans, etc.), patient records, genetic data, and streamline IoT data—the need for advanced image processing and data management techniques has never been more urgent. In addition, distributed learning strategies associated with privacy concerns and data heterogeneity also need to be handled. Medical systems must be able to deal with large, diverse datasets that are often multimodal, unstructured, and heterogeneous.

Efficient processing, analysis, and retrieval of medical images and associated data are critical in providing timely and accurate diagnoses, personalized medicine, and enhanced treatment monitoring. Moreover, with the rapid adoption of artificial intelligence (AI) and deep learning models, there is an increasing focus on improving the accuracy and interpretability of AI models for healthcare applications. This Special Issue seeks to explore the latest advancements in data management and image processing techniques that enable intelligent healthcare solutions, from early detection to personalized treatment.

In this context, large data management guided by decentralized learning strategies like distributed learning and federated learning could be used. Indeed, while distributed learning relies on centralized data storage and communication, federated learning emphasizes decentralized data management and privacy, making it more suitable for scenarios where data security is a priority.

This Special Issue will highlight cutting-edge research and developments aimed at overcoming the technological and computational challenges in healthcare data management and medical image processing. The goal is to showcase innovative methods, solutions, and practical applications that can enhance the effectiveness of healthcare delivery through advanced artificial intelligence, data management and image processing techniques. This Special Issue will also explore recent developments in the application of federated and or distributed learning for the diagnosis and management of medical diseases. We invite contributions that investigate new approaches, methodologies, and challenges in deploying AI-based models in clinical environments, as well as research that highlights its benefits in improving healthcare outcomes.

Potential topics for this Special Issue include, but are not limited to, the following:

• Deep learning for medical image analysis;
• AI-based healthcare applications (e.g., diagnostics, predictive modeling);
• Advanced image processing techniques for medical imaging;
• Multimodal medical image retrieval and analysis;
• Three-dimensional medical imaging and analysis;
• Medical image segmentation and object detection;
• Real-time processing and decision support in healthcare systems;
• AI for personalized medicine and treatment recommendations;
• Medical data privacy and security management;
• Edge computing for healthcare data processing;
• Telemedicine and remote monitoring using image analysis;
• Challenges in multimodal healthcare data management;
• Distributed learning in medical imaging;
• Federated learning for early detection and diagnosis;
• Data privacy, security, and regulatory considerations in federated learning;
• Collaborative model training across multiple healthcare institutions for disease diagnosis;
• Multi-center federated learning for large-scale medical image analysis;
• Handling data heterogeneity and imbalance in federated learning for healthcare;
• Federated learning for personalized medicine in breast cancer and cardiac diseases;
• Ethical implications of federated learning in healthcare;
• Federated learning for healthcare data integration and decision support systems.

Prof. Dr. Saïd Mahmoudi
Guest Editor

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• deep learning
• medical image analysis
• segmentation
• federated learning
• distributed learning
• multimodal learning