Health Editor’s Note: We use artificial intelligence (AI) in much of our daily lives. Smart phones, social media feeds, smart cars, music, drones, media streaming, video games, online ads network, banking and finance, travel and navigation, security, surveillance, and smart home devices are all forms of AI. Now AI will become an important asset for the medical imaging world…Carol
National experts chart roadmap for AI in medical imaging
By National Institutes of Health
A foundational research roadmap for artificial intelligence (AI) in medical imaging was published this week in the journal Radiology. The report was based on outcomes from a workshop to explore the future of AI in medical imaging, featuring experts in medical imaging, and hosted at the National Institutes of Health in Bethesda, Maryland. The workshop was co-sponsored by the National Institute of Biomedical Imaging and Bioengineering, the Radiological Society of North America, the American College of Radiology, and the Academy for Radiology and Biomedical Imaging Research.
The collaborative report underscores the commitment by standards bodies, professional societies, governmental agencies, and private industry to work together to accomplish a set of shared goals in service of patients, who stand to benefit from the potential of AI to bring about innovative imaging technologies.
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. The roadmap of priorities for AI in medical imaging research includes:
- 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 artificial intelligence), and
- validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets.
Langlotz, CP, et al. A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology. April 16, 2019.
Co-authors of the report with Curtis P. Langlotz were Bibb Allen, M.D.; Bradley J. Erickson, M.D., Ph.D.; Jayashree Kalpathy-Cramer, Ph.D.; Keith Bigelow, B.A.; Tessa S. Cook, M.D., Ph.D.; Adam E. Flanders, M.D.; Matthew P. Lungren, M.D., M.P.H.; David S. Mendelson, M.D.; Jeffrey D. Rudie, M.D., Ph.D.; Ge Wang, Ph.D.; and Krishna Kandarpa, M.D., Ph.D.
Kris Kandarpa, M.D., Ph.D., Director of Research Sciences and Strategic Directions at NIBIB, is available for comment.
About the National Institute of Biomedical Imaging and Bioengineering: NIBIB’s mission is to improve health by leading the development and accelerating the application of biomedical technologies. The Institute is committed to integrating the physical and engineering sciences with the life sciences to advance basic research and medical care. NIBIB supports emerging technology research and development within its internal laboratories and through grants, collaborations, and training. More information is available at the NIBIB website: http://www.nibib.nih.gov.
About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.
Carol graduated from Riverside White Cross School of Nursing in Columbus, Ohio and received her diploma as a registered nurse. She attended Bowling Green State University where she received a Bachelor of Arts Degree in History and Literature. She attended the University of Toledo, College of Nursing, and received a Master’s of Nursing Science Degree as an Educator.
She has traveled extensively, is a photographer, and writes on medical issues. Carol has three children RJ, Katherine, and Stephen – one daughter-in-law; Katie – two granddaughters; Isabella Marianna and Zoe Olivia – and one grandson, Alexander Paul. She also shares her life with her husband Gordon Duff, many cats, and two rescues.