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.
Author
(s) Details
Divyesh Goswami
Department of Pathology, Nootan Medical College and Research
Center, Visnagar, Gujarat, India.
Amar C Sajjan
Department of Microbiology, Chalmeda Anand Rao Institute of Medical
Sciences, Karimnagar, Telangana, India.
Ruchi Yadav
Department of Pathology, Maharishi Chyawan Government Medical College
Koriyawas, Mahendergarh, Narnaul, Haryana, India.
Nipa Hathila
Department of Radiodiagnosis, Pacific Medical College and Hospital,
Udaipur, Rajasthan, India.
Please see the book here:- https://doi.org/10.9734/bpi/msraa/v4/5423
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