A Comprehensive Review on Disease Detection and Interpretation of Biomedical Imaging Using AI

The introduction of Artificial Intelligence (AI) in medical imaging is considered to be one of the most important processing advances in current clinical diagnostics. This comprehensive review article presents a timely summary of the use of AI paradigms, namely deep learning and its models, such as Convolutional Neural Networks (CNNs), U-Net, and Vision Transformers, in key image segmentation, disease finding, and clinical interpretation tasks. Included in the review are many types of imaging: MR Imaging (MRI), Computed Tomography (CT), digital pathology, ultrasound, and fundus photography. A systematic literature review illustrates that artificial intelligence (AI) models have already reached or exceeded human experts in diagnostic performance with a focus on diseases such as adenocarcinomas (cancer), neurological disorders, cardiovascular disease, and retinal pathologies [1]. Nevertheless, there still exist the main challenges of generalization across clinical settings, “black-box” characteristics, lack of explainability and interpretability for deep learning models, and easy integration with clinical interpretation for daily practice. this work points out important research frontiers that need to be addressed in the future, such as Explainable AI (XAI) models, privately and securely federated learning schemes as well as multimodal data fusion techniques and standardized evaluation architectures. By laying out a structured pathway that combines high-performance segmentation architectures with glass-box interpretability layers and formal validation protocols, this review aims at influencing the future development of research. Tackling these core hindrances is likely to accelerate AI’s evolution from an adjunct diagnostic tool into a necessary, interpretable, and ethically responsible foundation of clinical practice — serving to significantly improve accuracy with which diagnoses are made, as well as patient health outcomes worldwide.