Qure.ai backs education to help smooth diagnostic AI adoption

Building on its endorsement by The College of Radiographers for CPD accredited super user training, Qure.ai is extending its links with UK health academia and industry to ensure skills are in place to support rapid AI adoption. Continuous learning through robust education initiatives are intended to prepare radiology workforces and propagate the full potential of AI into the future.

A number of educational webinars and in-person theory and practical training sessions are planned. This includes Qure.ai’s second participation in the City, University of London Introduction to Artificial Intelligence for Radiographers professional development course in November. Director of the postgraduate radiography programme Dr Christina Malamateniou said: “Practical sessions incorporated into our courses from AI providers ensure that radiographers and students are well prepared for the latest innovations available. It also helps stimulate discussion and debate to understand and be ready for implementation practicalities such as accountability and patient acceptability. We greatly appreciate the time Qure.ai gives to our student courses and look forward to welcoming it back later this year.”

Qure.ai senior vice president and commercial head UK and Europe Darren Stephens added: “As the pace of AI activation quickens, the need to support diagnostic workforces with knowledge and skills is essential. AI can deliver any time and generate efficiencies for the radiology department, which is a huge opportunity. At the same time, it will empower the role of radiographers and provide radiologists with much needed time bandwidth to focus on speciality or complex cases.

“By ensuring workforces are supported into the future with knowledge and skills, AI will be central to creating positive transformative partnerships.”

Picture: Director of the postgraduate radiography programme at City, University of London, Dr Christina Malamateniou with Louise Dillion who was student recipient of the 2023 Qure.ai award for excellent academic performance in the AI for Radiographers module of the master’s in radiography.

Published on page 8 of the September 2023 issue of RAD Magazine.