Artificial Intelligence in Radiology and Pathology: Transforming Medical Imaging Interpretation

Abstract

Artificial intelligence (AI) has revolutionised the fields of radiology and pathology, significantly enhancing the accuracy, efficiency, and accessibility of medical imaging interpretation. AI-driven algorithms, particularly deep learning and machine learning models, have demonstrated remarkable capabilities in detecting, classifying, and segmenting pathological findings in medical images, including X-rays, CT scans, MRIs, and histopathological slides. These advancements not only aid in early disease detection and diagnosis but also facilitate workflow optimisation, reducing radiologists' and pathologists' workload. Furthermore, AI-driven predictive models contribute to precision medicine by enabling personalised treatment plans. However, challenges such as data privacy, ethical concerns, and the need for robust validation limit widespread clinical adoption. This review explores the current applications of AI in radiology and pathology, its impact on diagnostic accuracy, and the challenges that must be addressed for seamless integration into clinical practice.

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