Smart Medical Image Processing System Based on Explainable and Generative Artificial Intelligence: A Comprehensive Review

In recent years, the integration of advanced methods in medical imaging has become a major topic of interest due to its potential to enhance diagnostic accuracy, improve clinical efficiency, and increase specialists’ confidence in Artificial Intelligence (AI)-based decision-making. This paper explores the synthesis of Explainable AI (XAI) and Generative AI (GAI) in medical imaging, highlighting the advantages and challenges of these emerging technologies. The objective of this paper is to explore how the combined use of XAI and GAI contributes both to interpretability and to diagnostic accuracy. This research represents a systematic literature review conducted in accordance with PRISMA 2020, based on searches carried out in the PubMed, Scopus, IEEE Xplore, MDPI and ScienceDirect databases. Thus, a comprehensive overview of the integration of XAI and GAI in medical imaging is presented, based on recent studies and validated clinical applications. The advantages of combining transparency and data amplification in diagnostic models are highlighted, demonstrating their complementary roles in improving diagnosis using medical imaging. Ongoing challenges in clinical adoption are also emphasised, including interpretability and the need for validated assessment metrics. Beyond technological benefits, the paper also underlines the importance of ethical and legal considerations in the use of XAI and GAI in medical imaging. Based on the detailed analysis of the investigated studies, the paper also proposes a visual and architectural system concept intended for medical imaging, oriented towards research into the development of a unified system capable of detecting multiple types of pathologies. This research provides a detailed perspective on how XAI and GAI can revolutionise medical imaging by optimising data interpretation, enhancing human-AI collaboration, and increasing patient safety.