An Artificial Intelligence Training Workshop for Diagnostic Radiology Residents
Bibliographic record
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".