The Evolution of Artificial Intelligence in Diagnostic Imaging: Challenges and Perspectives

To synthesize the state of the art and the impacts of artificial intelligence (AI) on medical diagnosis by analyzing performance techniques, operational efficiency and solutions in explainable artificial intelligence (XAI). Methods: This was a review of reviews (Umbrella Review) based on the PRISMA guidelines. The search was carried out in the PubMed and Scopus databases and covered the period from 2022 to 2026, resulting in the selection of ten articles. Results: The AI achieved an accuracy greater than 95% in classification tasks and a 30% to 50% reduction in segmentation time. in radiotherapy. The fusion of multimodal data surpasses modality models uniquely, although there are persisting standardization challenges and black box phenomena. Conclusion: AI is an indispensable pillar of precision medicine. Success in clinical practice depends on greater methodological rigor, greater external validation, and the adoption of models.intrinsically self-explanatory.