A systematic review on the impact of machine learning on medical diagnostic imaging. (#2035)

Authors

Duque Meza, Hector Ivan

Abstract

This systematic literature review (SLR) aims to analyze medical image processing techniques based on artificial intelligence (AI) and machine learning to improve diagnostic accuracy and efficiency in medical institutions. Based on a search of the SCOPUS database, 2757 articles were identified, from which, after applying inclusion and exclusion criteria, 53 relevant studies were selected. The results show that machine learning techniques overcome the limitations of traditional methods by increasing diagnostic accuracy and reducing processing times. Major advances include the use of deep learning algorithms and automation in image analysis, although challenges remain, such as reliance on trained operators, technical constraints in certain institutions, and the need for broader clinical validation. In conclusion, SLR highlights the transformative potential of AI in medical diagnostics, with the potential to optimize clinical processes and outcomes. However, to maximize its effectiveness, it is crucial to overcome the identified technological and implementation barriers, thus ensuring its sustainable integration into medical practice.