Advanced Deep Learning and Hybrid Architectures in Biomedical Data Analysis for Advances in Medicine

Abstract

Traditional diagnostic methods in biomedicine are often constrained by subjectivity, misdiagnosis, and the inability to efficiently process large, complex, and multimodal datasets. Recent advances in deep learning and hybrid architectures have enabled the automated learning of discriminative representations from high‐dimensional biomedical data, supporting robust classification and integrative analysis across imaging, physiological signals, and clinical texts. This review synthesizes the current progress in applying deep learning to neuroimaging, cardiovascular disease diagnosis, functional connectivity analysis, and biomedical text mining, focusing on hybrid and ensemble strategies that combine complementary modeling strengths. These approaches show improved accuracy, scalability, and adaptability while extending situational awareness by incorporating patient‐generated and clinical textual data. Despite promising outcomes, challenges remain in data availability, computational efficiency, interpretability, and clinical integration. Future directions emphasize the development of explainable, multimodal, and generalizable frameworks capable of supporting precision medicine and advancing patient‐centered care.