Artificial intelligence in diagnostic and interventional radiology: Where are we now?

The emergence of massively parallel yet affordable computing devices has been a game changer for research in the field of artificial intelligence (AI). In addition, dramatic investment from the web giants has fostered the development of a high-quality software stack. Going forward, the combination of faster computers with dedicated software libraries and the widespread availability of data has opened the door to more flexibility in the design of AI models. Radiomics is a process used to discover new imaging biomarkers that has multiple applications in radiology and can be used in conjunction with AI. AI can be used throughout the various pro-cesses of diagnostic imaging, including data acquisition, reconstruction, analysis and reporting. Today, the concept of AI-augmented radiologists is preferred to the theory of the replacement of radiologists by AI in many indications. Current evidence bolsters the assumption that AI-assisted radiologists work better and faster. Interventional radiology becomes a data-rich specialty where the entire procedure is fully recorded in a standardized DICOM format and accessible via standard picture archiving and communication systems. No other interventional specialty can bolster such readiness. In this setting, interventional radiology could lead the development of AI-powered applications in the broader interventional community. This article provides an update on the current status of radiomics and AI research, analyzes upcoming challenges and also dis-cusses the main applications in AI in interventional radiology to help radiologists better understand and criti-cize articles reporting AI in medical imaging.(c) 2022 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Automated summary

The emergence of affordable massively parallel computing devices has revolutionized AI research. The investment from web giants has also contributed to the development of high-quality software. The combination of faster computers, dedicated software libraries, and abundant data has increased flexibility in AI model design. Radiomics and AI are used in diagnostic imaging, and current evidence suggests that AI-assisted radiologists work better and faster. Interventional radiology, with its rich data and standardized format, has the potential to lead the development of AI-powered applications. This article provides an update on the status of radiomics and AI research and discusses their applications in interventional radiology.