Computational Intelligence for Multimodal Biomedical Data Fusion

The integration of artificial intelligence (AI) and computational intelligence techniques has revolutionized biomedical signal processing by enabling more precise disease diagnostics and patient monitoring. However, single-source biomedical data often lack the necessary depth and context for accurate medical decision-making. Multimodal biomedical data fusion—which combines signals from various sources such as electrocardiograms (ECG), electroencephalograms (EEG), medical imaging (MRI, CT scans), genetic biomarkers, and wearable sensor data—has emerged as a powerful approach to enhance diagnostic accuracy and clinical insights. Advances in computational intelligence, including deep learning, machine learning, and probabilistic models, are driving innovation in this field. By integrating diverse data sources, AI-powered systems can provide more comprehensive assessments, reduce false positives, and support personalized medicine. This Research Topic aims to explore the latest developments in computational intelligence techniques for multimodal biomedical data fusion and their potential applications in next-generation healthcare systems.

Despite the increasing availability of diverse biomedical data, the challenge of effectively integrating multimodal signals remains a significant hurdle. Traditional analysis techniques often struggle with heterogeneous data structures, varying temporal resolutions, and noise inherent in biomedical signals. Moreover, ensuring interpretability and trust in AI-driven diagnostic models is crucial for clinical adoption. This Research Topic seeks to address these challenges by exploring novel computational intelligence methodologies for multimodal biomedical data fusion. We aim to investigate:

- Advanced machine learning and deep learning models for integrating multiple biomedical data types.
- Explainable AI (XAI) techniques to enhance transparency in medical decision-making.
- Real-time multimodal data processing for wearable health monitoring systems.
- Federated learning and privacy-preserving AI models for secure data fusion.
- AI-driven approaches for early disease detection, precision medicine, and personalized healthcare.

By bringing together innovative contributions from researchers and practitioners, this Research Topic will provide a comprehensive overview of the state-of-the-art methods and emerging trends in computational intelligence for multimodal biomedical data fusion.

This Research Topic invites original research articles, reviews, and case studies focusing on the development and application of computational intelligence techniques for multimodal biomedical data fusion. We encourage contributions in the following areas:

1. AI Models for Multimodal Fusion: Deep learning, reinforcement learning, and hybrid AI techniques for fusing biomedical signals.
2. Medical Imaging and Signal Processing: AI-driven integration of medical imaging (MRI, CT) with physiological data (EEG, ECG, EMG).
3. Wearable and IoMT-Based Healthcare Solutions: Multimodal data fusion in real-time health monitoring and remote diagnostics.
4. Explainable and Trustworthy AI: Transparent models for medical decision-making and regulatory compliance.
5. Privacy-Preserving and Federated Learning Approaches: Secure data fusion methods in distributed healthcare environments.

We welcome contributions from interdisciplinary domains, including artificial intelligence, biomedical engineering, healthcare informatics, and computational neuroscience. Manuscripts should present novel methodologies, experimental validations, or real-world applications that advance the field of multimodal biomedical data fusion.