The Role of Artificial Intelligence in Improving Diagnostic Accuracy in Medical Imaging: A Review

This review comprehensively analyzes advancements in artificial intelligence, particularly machine learning and deep learning, in medical imaging, focusing on their transformative role in enhancing diagnostic accuracy. Our in-depth analysis of 138 selected studies reveals that artificial intelligence (AI) algorithms frequently achieve diagnostic performance comparable to, and often surpassing, that of human experts, excelling in complex pattern recognition. Key findings include earlier detection of conditions like skin cancer and diabetic retinopathy, alongside radiologist-level performance for pneumonia detection on chest X-rays. These technologies profoundly transform imaging by significantly improving processes in classification, segmentation, and sequential analysis across diverse modalities such as X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound. Specific advancements with Convolutional Neural Networks, Recurrent Neural Networks, and ensemble learning techniques have facilitated more precise diagnosis, prediction, and therapy planning. Notably, Generative Adversarial Networks address limited data through augmentation, while transfer learning efficiently adapts models for scarce labeled datasets, and Reinforcement Learning shows promise in optimizing treatment protocols, collectively advancing patient care. Methodologically, a systematic review (2015–2024) used Scopus and Web of Science databases, yielding 7982 initial records. Of these, 1189 underwent bibliometric analysis using the R package ‘Bibliometrix’, and 138 were comprehensively reviewed for specific findings. Research output surged over the decade, led by Institute of Electrical and Electronics Engineers (IEEE) Access (19.1%). China dominates publication volume (36.1%), while the United States of America (USA) leads total citations (5605), and Hong Kong exhibits the highest average (55.60). Challenges include rigorous validation, regulatory clarity, and fostering clinician trust. This study highlights significant emerging trends and crucial future research directions for successful AI implementation in healthcare.