Revolutionising Radiology: Integrating Artificial Intelligence for Enhanced Diagnostic Accuracy and Efficiency (KEY IJP************313)

• Tamanna Chidanand

Abstract

This review addresses the growing demand for radiological imaging amidst a shortage of skilled radiologists and declining imaging reimbursements. It focuses on how Artificial Intelligence (AI), alongside Machine Learning (ML) and Deep Learning (DL), has transformed the field of radiology. Tracing the evolution of AI from its inception in the 1950s to the present, we highlight key milestones such as the development of basic machine learning, natural language processing, and the rise of deep learning and neural networks.We provide a comprehensive overview of the AI workflow in radiology, covering stages from data acquisition to treatment planning and follow-up care, and demonstrate how AI optimises each stage for improved patient outcomes. Through a series of case studies, we substantiate the pivotal role of AI in increasing workflow efficiency and diagnostic accuracy.Challenges such as patient confidentiality, regulatory compliance, algorithmic bias, and the enigmatic 'black boxnature of AI models are acknowledged, presenting opportunities for further growth and improvement. The conclusion emphasises AI's transformative impact on radiology, underscoring its potential as a driving force for innovation and enhanced patient care.