A "Bumper-Car" Curriculum for Teaching Deep Learning to Radiology Residents.

RATIONALE AND OBJECTIVES

Our goal was to create an artificial intelligence (AI) training curriculum for residents that taught them to create, train, evaluate and refine deep learning (DL) models. Hands-on training of models was emphasized and didactic presentations of the mathematical and programmatic underpinnings of DL were minimized.

MATERIALS AND METHODS

We created a three-session, 6-hour curriculum based on a "no-code" machine learning system called Lobe.ai. This class met weekly in June 2021. Pre-class homework included reading assignments, software installation, dataset downloads, and image-collection and labeling. The class sessions included several short, didactic presentations, but were largely devoted to hands-on training of DL models. After the course, our residents completed a short, anonymous, online survey about the course.

RESULTS

Our residents learned to acquire and label a wide variety of image datasets. They quickly learned to train DL models to classify these datasets, as well as how to evaluate and refine these models. Our survey showed that most residents felt AI to be important and worth learning, but most were not very interested in learning to program. Most felt that the course taught them useful things about DL, and they were now more interested in the topic. Most would recommend the course to other residents, as well as to medical students and to radiology faculty.

CONCLUSION

The course met our objectives of teaching our residents to create, train, evaluate, and refine DL models. We hope that the hands-on experience they gained in this course will enable them to recognize problems in diagnostic AI systems, and to help solve such problems in their own radiology practices.