RSNA-MICCAI Panel Discussion: 2. Leveraging the Full Potential of AI-Radiologists and Data Scientists Working Together

Marius George Linguraru; Lena Maier-Hein; Ronald M Summers; Charles E Kahn Jr

doi:10.1148/ryai.2021210248

RSNA-MICCAI Panel Discussion: 2. Leveraging the Full Potential of AI-Radiologists and Data Scientists Working Together

Radiol Artif Intell. 2021 Oct 27;3(6):e210248. doi: 10.1148/ryai.2021210248. eCollection 2021 Nov.

Authors

Marius George Linguraru¹, Lena Maier-Hein¹, Ronald M Summers¹, Charles E Kahn Jr¹

Affiliation

¹ Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC (M.G.L.); Department of Computer Assisted Medical Interventions, German Cancer Research Centre, Heidelberg, Germany (L.M.H.); Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of Radiology, University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA 19104 (C.E.K.).

Abstract

In March 2021, the Radiological Society of North America hosted a virtual panel discussion with members of the Medical Image Computing and Computer Assisted Intervention Society. Both organizations share a vision to develop radiologic and medical imaging techniques through advanced quantitative imaging biomarkers and artificial intelligence. The panel addressed how radiologists and data scientists can collaborate to advance the science of AI in radiology. Keywords: Adults and Pediatrics, Segmentation, Feature Detection, Quantification, Diagnosis/Classification, Prognosis/Classification © RSNA, 2021.

Keywords: Adults and Pediatrics; Diagnosis/Classification; Feature Detection; Prognosis/Classification; Quantification; Segmentation.

2021 by the Radiological Society of North America, Inc.

Publication types

Editorial