Artificial Intelligence in Radiology: Transforming Diagnostic Accuracy and Workflow Efficiency

Artificial intelligence (AI) is fundamentally transforming radiology by enhancing diagnostic precision and optimizing workflow efficiency. This comprehensive review synthesizes evidence from 128 clinical studies and industry implementations to evaluate AI’s impact across medical imaging domains. Deep learning algorithms, particularly convolutional neural networks (CNNs) and transformer architectures, now demonstrate expert-level performance in detecting lung nodules, breast malignancies, and neurological emergencies—reducing false negatives by 27-35% and improving early disease detection. Workflow integration of AI enables automated image triage (reducing critical result notification time by 1.7 hours), protocol optimization (decreasing scan times by 30%), and structured reporting (cutting interpretation time by 37%). Significant challenges persist, including dataset bias affecting generalizability, the “black-box” nature of complex algorithms, and regulatory hurdles in real-world validation. Multimodal AI systems integrating imaging with genomic and clinical data represent the next frontier in personalized diagnostics. Successful implementation requires stakeholder engagement, continuous education, and interoperability standards. As AI matures beyond augmentation toward autonomous detection of subvisual biomarkers, radiologists will evolve into diagnostic orchestrators overseeing AI-enhanced workflows. This transformation demands ethical frameworks ensuring equitable access and transparent validation of AI tools across diverse populations.