Levels of Autonomous Radiology
This work addresses the need for structured frameworks to guide AI adoption in radiology, which is incremental as it builds on existing automation concepts.
The paper tackles the challenge of integrating AI into radiology by proposing a level-wise classification for automation progression, aiming to structure discussions on AI assistance, challenges, and solutions to facilitate smooth technology adoption.
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 (AI) 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 cases to aid radiologists in their evaluation workflow. We propose a level-wise classification for the progression of automation in radiology, explaining AI assistance at each level with corresponding challenges and solutions. We hope that such discussions can help us address the challenges in a structured way and take the necessary steps to ensure the smooth adoption of new technologies in radiology.