A systematic review on the impact of machine learning on medical diagnostic imaging

Ivan Duque Meza H. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2025) · DOI: 10.18687/laccei2025.1.1.2035

Resumen

This is a systematic literature review (SLR) that aims to identify and analyze medical image processing techniques using artificial intelligence (AI) and machine learning to improve the accuracy and speed of diagnostic imaging in medical institutions. To this end, SCOPUS selected 2757 articles using inclusion and exclusion criteria, resulting in 53 relevant articles. In addition, the findings are grouped into key themes, highlighting the growing interest in the use of artificial intelligence in medical diagnosis. Therefore, the results indicate that machine learning can overcome the limitations of traditional methods, improve diagnostic accuracy and reduce waiting times; however, some problems were observed, such as dependence on operator expertise and limited technical infrastructure. Therefore, SLR highlights the importance of integrating AI technology into diagnostic imaging, suggesting that its implementation has the potential to transform medical practice, although challenges need to be addressed to maximize its effectiveness.

Instituciones: Universidad Tecnológica del Perú