Levels of Autonomous Radiology

Abstract

Radiology, being one of the younger disciplines of medicine with a history of just over a century, has witnessed tremendous technological advancements and has revolutionized the way we practice medicine today. In the last few decades, medical imaging modalities have generated seismic amounts of medical data. The development and adoption of artificial intelligence applications using this data will lead to the next phase of evolution in radiology. It will include automating laborious manual tasks such as annotations, report generation, etc, along with the initial radiological assessment of patients and imaging features to aid radiologists in their diagnostic and treatment planning workflow. We propose a level-wise classification for the progression of automation in radiology, explaining artificial intelligence assistance at each level with the corresponding challenges and solutions. We hope that such discussions can help us address challenges in a structured way and take the necessary steps to ensure the smooth adoption of new technologies in radiology.

Keywords: AI assistance; artificial intelligence; automation; autonomous radiology; distributed learning; explainability; fairness and bias; generalizability; machine learning; model decay; radiology.

Figure 1

Flowchart depicting the various levels of automation in radiology practice. At each level, the role of the radiologist and artificial intelligence (AI) is outlined, along with the enabling factors required to mitigate the potential challenges for progression to the next level. PACS: picture archiving and communication systems.