JMIR Publications

ABSTRACT

Radiology is undergoing a transformation thanks to artificial intelligence, especially deep learning, which improves diagnostic accuracy, expedites workflows, and aids in clinical decision-making. Traditional radiology primarily relies on human expertise and visual pattern recognition, whereas AI, particularly convolutional neural networks, can now detect, classify, and quantify abnormalities in medical images with performance on par with or even better than that of seasoned radiologists. The primary aim of integrating AI into radiology is to enhance diagnostic precision, streamline clinical workflows, and support radiologists in decision-making. Additional objectives include improving image quality, facilitating urgent patient triage, and enabling personalized treatment planning. AI applications are expected to help provide quantitative insights through radiomics and increase access to care, even in under-resourced regions. Artificial intelligence in radiology encompasses machine learning and its more advanced form, deep learning, which use layered neural networks to extract intricate patterns from large datasets. These technologies excel in tasks such as image classification, lesion detection, segmentation, and disease prognosis. Innovations like multimodal learning, explainable AI, federated learning, and integration with real-time PACS systems have significantly improved the safety and utility of AI in clinical environments. Commercial tools such as Qure.ai, Zebra Medical Vision, and Aidoc have already demonstrated practical effectiveness across a variety of healthcare setting. Despite its promising capabilities, AI in radiology faces challenges including data bias, transparency issues, legal hurdles, and the necessity for thorough clinical validation. Ethical considerations, such as patient privacy, equitable access, and liability, must also be addressed. Looking forward, AI is expected to complement rather than replace radiologists, acting as an intelligent assistant. The integration of AI with genetics, laboratory data, and global health initiatives holds the potential to enable precision radiology and improve overall patient outcomes. Realizing this vision will require collaborative, ethical, and responsible implementation.