top of page

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA

Adrian P. Brady, Bibb Allen

Published Online: Jan 22, 2024 RSNA: Radiology: Artificial Intelligence


We've been excited to see, digest and react to the Special Report in Radiology: Artificial Intelligence representing a practical overview of considerations, guidance and recommendations for developers, users, procurers, and regulators of AI tools applied in radiology [1]. It is an extremely useful compendium of reference material, real-world experiences, considerations, and advice to the (very broad) community implicated in the lifecycle of AI technologies in medical imaging. As a professional services and solutions firm, we resonate with and appreciate nearly all the recommendations, observations, and considerations throughout the report.


Each of the societies and their respective members and institutions have been pioneers in the development and dissemination of both evolutionary and revolutionary digital technologies into the daily practice of medicine since the 1970s. It was not just catalyzed by the recent wave of excitement arising from the last and current generation of data-driven, computational methods like Deep Learning. Radiology is arguably the only sub-specialty in medicine that is - and has been - 100% 'digital' for nearly 20 years. CT, Ultrasound, and MRI, for example, are inherently digital; there would be no medical images without advanced mathematical computations, digital signal processing, and networking. Therefore, while many areas of healthcare are recently now wrangling with the regulatory, practical, and IT issues of embracing "AI" into the clinic the collective experience of the radiology community, especially as encoded into reports like this one, should be broadly referred and built-upon. Follow the link to find the full article.


bottom of page