Stakeholders gather in London to discuss development of AI in radiology, oncology

Radiologists, clinical oncologists and industry stakeholders gathered May 16 in London to discuss artificial intelligence (AI) in medical imaging and cancer treatment. The all-day event was organized by the Royal College of Radiologists (RCR) with help from the Alan Turing Institute, Health Data Research UK and the Engineering and Physical Sciences Research Council.

The event, “Grand challenges in artificial intelligence in clinical radiology and clinical oncology,” was originally the idea of Nicola Strickland, MD, president of the RCR, according to a prepared statement.

“Our key stakeholder meeting demonstrated that the UK has a real opportunity to take the lead on artificial intelligence programs in healthcare, if clinicians and researchers are brave enough to embrace its potential and work with industry to shape the application of machine learning in practice,” Strickland said in the statement. “For clinical radiologists and oncologists to survive and thrive in the swelling digital revolution, it is vital these medical specialties work in collaboration with funders, AI experts and industry to develop AI that is robustly tested and regulated so it can be confidently put into practice to augment the work of clinicians and bring greater benefit to patients.”

More than 100 stakeholders participated in the event. Lord Ara Darzi, a well-known surgeon, was a keynote speaker, encouraging collaboration among stakeholders.

Michael Walter
Michael Walter, Managing Editor

Michael has more than 18 years of experience as a professional writer and editor. He has written at length about cardiology, radiology, artificial intelligence and other key healthcare topics.

Around the web

The ACR hopes these changes, including the addition of diagnostic performance feedback, will help reduce the number of patients with incidental nodules lost to follow-up each year.

And it can do so with almost 100% accuracy as a first reader, according to a new large-scale analysis.

The patient, who was being cared for in the ICU, was not accompanied or monitored by nursing staff during his exam, despite being sedated.