Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents.

Journal Article (Journal Article)

Artificial intelligence and machine learning (AI-ML) have taken center stage in medical imaging. To develop as leaders in AI-ML, radiology residents may seek a formative data science experience. The authors piloted an elective Data Science Pathway (DSP) for 4th-year residents at the authors' institution in collaboration with the MGH & BWH Center for Clinical Data Science (CCDS). The goal of the DSP was to provide an introduction to AI-ML through a flexible schedule of educational, experiential, and research activities. The study describes the initial experience with the DSP tailored to the AI-ML interests of three senior radiology residents. The authors also discuss logistics and curricular design with common core elements and shared mentorship. Residents were provided dedicated, full-time immersion into the CCDS work environment. In the initial DSP pilot, residents were successfully integrated into AI-ML projects at CCDS. Residents were exposed to all aspects of AI-ML application development, including data curation, model design, quality control, and clinical testing. Core concepts in AI-ML were taught through didactic sessions and daily collaboration with data scientists and other staff. Work during the pilot period led to 12 accepted abstracts for presentation at national meetings. The DSP is a feasible, well-rounded introductory experience in AI-ML for senior radiology residents. Residents contributed to model and tool development at multiple stages and were academically productive. Feedback from the pilot resulted in establishment of a formal AI-ML curriculum for future residents. The described logistical, planning, and curricular considerations provide a framework for DSP implementation at other institutions. Supplemental material is available for this article. © RSNA, 2020.

Full Text

Duke Authors

Cited Authors

  • Wiggins, WF; Caton, MT; Magudia, K; Glomski, S-HA; George, E; Rosenthal, MH; Gaviola, GC; Andriole, KP

Published Date

  • November 2020

Published In

Volume / Issue

  • 2 / 6

Start / End Page

  • e200057 -

PubMed ID

  • 33937848

Pubmed Central ID

  • PMC8082300

Electronic International Standard Serial Number (EISSN)

  • 2638-6100

Digital Object Identifier (DOI)

  • 10.1148/ryai.2020200057


  • eng

Conference Location

  • United States