A new report, with contributions from the national institute of biomedical imaging and bioengineering (NIBIB), part of the national institutes of health, provides a roadmap for translational research on artificial intelligence (AI) in medical imaging.Gagne's cognitive learning theory the report, published in the journal of the american college of radiology, identifies research priorities that leverage big data, the cloud, and machine learning for augmenting clinicians’ image planning and use to make diagnoses or assess patients’ responses to therapy.Gagne's cognitive learning theory

This report and a companion report published last month summarize conclusions from an august 2018 workshop co-organized by the NIH, the radiologic american college of radiology (ACR), the radiological society of north america (RSNA), and the academy for radiology and biomedical imaging research.Gagne's cognitive learning theory the first report published april 16, 2019, maps a path forward for foundational research in AI and this second report focuses on translational research necessary to deliver AI to clinical practice.Gagne's cognitive learning theory

“radiology has transformed the practice of medicine in the past century, and AI has the potential to radically impact radiology in positive ways,” says krishna kandarpa, MD, phd, co-author of the report and director of research sciences and strategic directions at NIBIB.Gagne's cognitive learning theory “this roadmap is a timely survey and analysis by experts at federal agencies and among our industry and professional societies that will help us take the best advantage of AI technologies as they impact the medical imaging field.”

gagne's cognitive learning theory

“this new roadmap paper gives guidance for the clinical translation of AI innovation,” said curtis P. Langlotz, MD, phd, report co-author and RSNA board liaison for information technology and annual meeting.Gagne's cognitive learning theory “together, these two connected roadmaps show us how AI not only will transform the work of radiologists and other medical imagers, but also will enhance the delivery of care throughout the clinical environment.”

gagne's cognitive learning theory

The authors suggest that the application of AI can impact the entire radiology process, from the clinical decision to perform diagnostic imaging, to preparation of patients for procedures, to conducting the scan, to interpretation of imaging results, and finally to the management of workflow in radiology departments.Gagne's cognitive learning theory while most attention is drawn to the power of computers to aid in interpreting images, there is ongoing research on AI applications that address every aspect of the imaging process, according to the report.Gagne's cognitive learning theory

“although advances in foundational research are occurring rapidly, translation to routine clinical practice has been slower because we must ensure AI in medical imaging is useful, safe, effective, and easily integrated into existing radiology workflows before they can be used in routine patient care,” says bibb allen, MD, report co-author and chief medical officer of the ACR data science institute.Gagne's cognitive learning theory

“this NIBIB sponsored workshop was an important step in coordinating private and government efforts related to AI implementation in medical imaging,” says mitchell schnall, MD phd, eugene P.Gagne's cognitive learning theory pendergrass professor & chair of radiology, university of pennsylvania; vice president and dxcp task force chair, academy of radiology & biomedical imaging research.Gagne's cognitive learning theory “it will take a true public-private partnership to realize the tremendous potential contribution of AI to transform medical imaging, and this roadmap is the first step in that direction.Gagne's cognitive learning theory