An Artificial Intelligence Training Workshop for Diagnostic Radiology Residents

Abstract

Purpose: To develop, implement, and evaluate feedback for an artificial intelligence (AI) workshop for radiology residents that has been designed as a condensed introduction of AI fundamentals suitable for integration into an existing residency curriculum.

Materials and methods: A 3-week AI workshop was designed by radiology faculty, residents, and AI engineers. The workshop was integrated into curricular academic half-days of a competency-based medical education radiology training program. The workshop consisted of live didactic lectures, literature case studies, and programming examples for consolidation. Learning objectives and content were developed for foundational literacy rather than technical proficiency. Identical prospective surveys were conducted before and after the workshop to gauge the participants' confidence in understanding AI concepts on a five-point Likert scale. Results were analyzed with descriptive statistics and Wilcoxon rank sum tests to evaluate differences.

Results: Twelve residents participated in the workshop, with 11 completing the survey. An average score of 4.0 ± 0.7 (SD), indicating agreement, was observed when asking residents if the workshop improved AI knowledge. Confidence in understanding AI concepts increased following the workshop for 16 of 18 (89%) comprehension questions (P value range: .001 to .04 for questions with increased confidence).

Conclusion: An introductory AI workshop was developed and delivered to radiology residents. The workshop provided a condensed introduction to foundational AI concepts, developed positive perception, and improved confidence in AI topics.Keywords: Medical Education, Machine Learning, Postgraduate Training, Competency-based Medical Education, Medical Informatics Supplemental material is available for this article. © RSNA, 2023.

Keywords: Competency-based Medical Education; Machine Learning; Medical Education; Medical Informatics; Postgraduate Training.

Graphical abstract

Figure 1:

(A) Overview of the workshop structure. Each week consists of didactic lectures, consolidated by case studies and programming examples relevant to the topics from the lecture. Supplemental material was provided for participants who wished to further pursue topics beyond the scope of the workshop. (B) Visualization of a programming example. In this specific example, a decision tree classifier is trained to predict malignancy from tumor images. The example highlights data science best practices in organizing input features, feature selection, model optimization, and validation. AI = artificial intelligence.

Figure 2:

Baseline characteristics of all the participants and postworkshop survey responses indicate resident confidence and interest. The participants included residents from postgraduate year (PGY) 1 to 4. None of the participants in the workshop had previous undergraduate training or professional experience in artificial intelligence (AI). Most participants reported that knowledge somewhat or strongly improved and were somewhat or very interested in continuing AI training in the future.

Figure 3:

Survey responses before and after the workshop. The questions gauge the confidence of participants in a particular topic, with three questions gauging the perception of the impact, importance, and interest of artificial intelligence. Responses were rated on a five-point Likert scale: 1 = strongly disagree, 2 = somewhat disagree, 3 = neutral, 4 = somewhat agree, and 5 = strongly agree. AI = artificial intelligence, DL = deep learning, ML = machine learning.