Radiology Publishes Roadmap for AI in Medical Imaging

Last August, a workshop was held at the National Institutes of Health (NIH) in Bethesda, MD, to explore the future of artificial intelligence (AI) in medical imaging. The workshop was co-sponsored by NIH, RSNA, the American College of Radiology (ACR) and The Academy for Radiology and Biomedical Imaging Research (The Academy).  The participants discussed how to foster collaboration in applications for diagnostic medical imaging, identify knowledge gaps and develop a roadmap to prioritize research needs. The group’s research roadmap was published as a special report in Radiology.

“The scientific challenges and opportunities of AI in medical imaging are profound, but quite different from those facing AI generally. Our goal was to provide a blueprint for professional societies, funding agencies, research labs, and everyone else working in the field to accelerate research toward AI innovations that benefit patients,” said the report’s lead author, Curtis P. Langlotz, MD, PhD.  Dr. Langlotz is a professor of radiology and biomedical informatics, director of the Center for Artificial Intelligence in Medicine and Imaging, and associate chair for information systems in the Department of Radiology at Stanford University, and RSNA Board liaison for information technology and annual meeting.

“RSNA’s involvement in this workshop is essential to the evolution of AI in radiology,” said Mary C. Mahoney, MD, RSNA Board of Directors chair. “As the Society leads the way in moving AI science and education forward through its journals, courses and more, we are in a solid position to help radiologic researchers and practitioners more fully understand what the technology means for medicine and where it is going.” 

Key research priorities identified for the next decade and beyond

In the report, the authors outline several key research themes, and describe a roadmap to accelerate advances in foundational machine learning research for medical imaging. 

Research priorities highlighted in the report include:

• new image reconstruction methods that efficiently produce images suitable for human interpretation from source data,
• automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting,
• new machine learning methods for clinical imaging data, such as tailored, pre-trained model architectures, and distributed machine learning methods,
• machine learning methods that can explain the advice they provide to human users (so-called explainable AI), and
• validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets.

The report describes innovations that would help to produce more publicly available, validated and reusable data sets against which to evaluate new algorithms and techniques, noting that to be useful for machine learning these data sets require methods to rapidly create labeled or annotated imaging data. 

In addition, novel pre-trained model architectures, tailored for clinical imaging data, must be developed, along with methods for distributed training that reduce the need for data exchange between institutions. 

In laying out the foundational research goals for AI in medical imaging, the authors stress that standards bodies, professional societies, governmental agencies and private industry must work together to accomplish these goals in service of patients, who stand to benefit from the innovative imaging technologies that will result.

Access the Radiology special report, “A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop,” at RSNA.pubs.org.