<p>This paper examines the interplay between artificial intelligence (AI) and quantum computing (QC) within the context of medical image analysis for modern diagnostics. In fact, while AI and particularly deep learning models have significantly advanced medical imaging, current limitations including non-polynomial computational complexity in training deep convolutional networks, scalability, and data heterogeneity are not yet completely resolved. However, QC, with its inherent parallelism and exceptional speedup capacity, presents a promising approach to address these limitations. The main aim of this study is therefore to take an in-depth look at the synergies between AI and QC, focusing on hybrid models that integrate quantum algorithms with AI capabilities for medical tasks such as segmentation, classification, and anomaly detection. To this end, we examine the theoretical foundations, emerging algorithmic paradigms, and practical frameworks that fuse AI and QC. This is conducted through a systematization of knowledge of recent advancements from both domains, while highlighting the significant potential of AI–QC integration to enable the principles of 5P Medicine: preventive, predictive, personalized, precision, and participatory health care. The proposed analysis lays a structured foundation for future research by identifying current gaps and offering insights that can guide the development of next-generation medical diagnostic tools powered by quantum AI technologies.</p>