Integrating machine learning into medical radiology: Principles, applications, challenges, and future directions

Abstract

Over recent decades, machine learning has been widely implemented in medical radiology. Radiologists, who are at the forefront of clinical practice, need to be aware of the benefits of machine learning to facilitate its implementation. It is crucial for them to thoroughly understand and effectively integrate machine learning into the practical realm of medical radiology. In this review, we highlight the principles and applications of machine learning in medical radiology and provide a summary of its development in this field. Machine learning has significantly advanced diagnostic imaging, enhancing detection, segmentation, and image reconstruction, while improving workflow efficiency and radiology reporting. Current literature indicates three primary challenges in implementing machine learning: data standardization, validation of model performance, and regulatory compliance. The successful integration of machine learning in clinical practice requires robust data security protocols and clear frameworks for professional accountability. To prepare for this technological transition, radiologists must develop new competencies through enhanced educational programs and adapt their roles to focus more on clinical decision-making and multidisciplinary collaboration while leveraging machine learning as a supportive tool.

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