@Radiology_AI

Many noninterpretive artificial intelligence applications with the potential to improve multiple aspects of radiology practice, including workflow, efficiency, image acquisition, reporting, billing, and education, are either currently available or in development. Artificial intelligence (AI) models to improve workflow efficiency and safety include automated clinical decision support, study protocoling, examination scheduling, and worklist prioritization. Models to improve image acquisition focus on patient positioning, multimodal image registration, dose reduction, noise reduction, and artifact reduction. Models to improve reporting include automatic finding categorization using classification systems (eg, Breast Imaging Reporting and Data System, Liver Imaging Reporting and Data System), provider notification of incidental findings, and closing the loop on patient follow-up. Business applications include automated billing and coding, obtaining preauthorization, and optimization of performance on quality measures to increase reimbursement. Use of AI in resident education is somewhat controversial, but AI can be used to help flag high-risk cases for faster review by an attending physician, customize teaching files based on residents' needs, and help improve resident reporting. The radiology community has had a leading role in exploring medical applications of artificial intelligence (AI), and one of the primary drivers for this is the desire for increased accuracy and efficiency in clinical care. Radiologist responsibilities extend beyond image interpretation. AI tools have the potential to improve essential tasks in the imaging value chain, from image acquisition to generating and disseminating radiology reports (1). These applications are crucial in current medical environments with increasing workloads, increasing scan complexity, and the need to decrease costs and reduce errors (2–4).