The Evolving Role Of Artificial Intelligence In Medical Imaging: A Comprehensive Review

Abstract

Artificial intelligence is fundamentally transforming the field of medical imaging, evolving from a conceptual tool into an integral component of modern diagnostic workflows. This paradigm shift is driven primarily by deep learning, which enables the automatic analysis of complex imaging data with superhuman precision for tasks ranging from detecting subtle pathologies to quantifying disease burden. AI applications now enhance every step of the imaging chain, from improving acquisition efficiency and automating segmentation to providing prognostic biomarkers through radiomics, thereby advancing the goals of precision medicine. However, this integration faces significant hurdles, including the need for large, curated datasets, risks of algorithmic bias, the "black box" nature of deep learning models, and challenges in clinical validation and workflow integration. The future of the field hinges on overcoming these obstacles through explainable AI, federated learning, and multimodal data fusion. Ultimately, AI is poised not to replace the radiologist but to create a synergistic partnership, augmenting human expertise with computational power to achieve more accurate, efficient, and personalized patient care.