Artificial Intelligence in Clinically Validated Medical Imaging: Transforming Radiological Practice and Diagnostic Accuracy

Artificial intelligence (AI) is transforming diagnostic radiology by enhancing image interpretation,pattern recognition, and clinical decision-making across multiple imaging modalities. This systematicliterature review critically evaluates the diagnostic performance, workflow efficiency, andimplementation challenges of AI-based imaging systems. A comprehensive search of PubMed,Scopus, Web of Science, and IEEE Xplore identified 11 high- and moderate-quality studiespublished between 2015 and 2025, following Preferred Reporting Items for Systematic Reviews andMeta-Analyses (PRISMA) 2020 guidelines. The analysis revealed that deep learning algorithms,particularly convolutional neural networks, reported diagnostic accuracies ranging from 85% to 98%across individual studies, with sensitivities in several applications exceeding 90%; these valuesrepresent descriptive ranges extracted from the included studies rather than pooled or weightedsummary estimates. AI applications demonstrated superior reproducibility and clinical reliability inmammography, chest radiography, and neuroimaging, where multi-institutional validation supportedconsistent outcomes. Despite these advancements, limitations persist due to inconsistent externalvalidation, dataset imbalance, and inadequate methodological transparency. Emerging frameworkssuch as explainable AI (XAI) and federated learning show potential to enhance interpretability, datasecurity, and equity in clinical deployment. Furthermore, AI integration was associated with reducedinterpretation time and improved workflow efficiency without compromising diagnostic accuracy.Overall, this review underscores AI’s transition from experimental innovation to a clinicallyindispensable tool. By synthesizing evidence across technical, clinical, and ethical dimensions, itprovides a comprehensive foundation for developing standardized, transparent, and equitable AImodels in diagnostic imaging practice.