Unlocking clinical insights from medical images using deep learning

Abstract

Medical imaging is fundamental to modern precision medicine and the analysis of complex image data requires sophisticated techniques. This review focuses on recent deep-learning techniques for medical image analysis across existing modalities such as X-rays, MRI, CT and ultrasound. The aim of this study was to review the current state-of-the-art methods, highlight proven applications in precision diagnostics and prognosis, analyze key challenges, and identify promising future directions for this rapidly advancing field. Convolutional Neural Networks (CNNs) enable transformative improvements in tasks such as classification, segmentation and detection compared with prior approaches. A wide range of real-world applications across diagnostic, interventional, prognostic and pharmaceutical settings has been presented. The salient challenges concerning model interpretability, multimodal integration, algorithmic robustness, workflow integration, and responsible ethical deployment were discussed. This synergy between medical imaging and artificial intelligence continues to unlock abundant clinically relevant insights latent in images and transforms datadriven precision medicine for patient benefit.