ROLE OF ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING: A SYSTEMATIC REVIEW

Abstract

This scholarly article provides a thorough overview of medical imaging modalities and their various uses in segmenting and classifying diseases using artificial intelligence (AI).This study provide a systematic review of research articles that use AI approaches to investigate illness classification and segmentation in various anatomical locations. Each article's results are carefully examined as part of the study, which also identifies new trends and extracts key insights. Additionally, the study provides a critical discussion of the 
difficulties observed in these investigations, including problems with quality and availability of data, generalization of the model, and interpretability. The objective of this study was to perform a systematic review of research publications that use AI approaches 
to investigate medical imaging, A database search was conducted using five online databases, including Web of Science, Scopus, Science Direct, Google Scholar, and Semantic Scholar, to identify relevant primary research on medical imaging and AI, using Boolean operators, The analysis emphasizes how important hybrid approaches are for obtaining meaningful and successful outcomes across a range of disease types. These approaches smoothly combine systematic procedures. Future research prospects in the field of medical diagnosis are made possible by the promising potential of these hybrid models. Furthermore, future research efforts should prioritize addressing the difficulties caused by the scarcity of annotated medical pictures by utilizing medical image synthesis and transfer learning approaches.