Machine learning enhances diagnostic accuracy in healthcare, indicating significant advancements in medical practices.
Machine learning has mostly helped doctors and analysts with their day-to-day work by assisting with tasks like spotting healthcare trends and creating illness prediction models. Large medical organizations have also begun to use machine learning-based approaches to better organize their electronic health records, detect abnormalities in organs, bones, and blood samples through medical imaging and monitoring, and to facilitate robot-assisted surgeries. The healthcare industry is rapidly adapting to the modern technological landscape, with many new advances appearing every year. The advancement of the area, which includes faster diagnostic times, more accurate results, and easier use, depends on AI and machine learning-based methods and applications. There is the use of machine learning to medical imaging, namely to MRIs, CAT scans, US scans, and PET scans. A radiologist is usually needed to analyze and diagnose the pictures that are produced by various imaging modalities. Machine learning methods have made great strides in predicting and locating photos that may reveal a disease status or major problem.
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Jain et al. (2025) studied this question.
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