The Growing Nationwide Radiologist Shortage: Current Opportunities and Ongoing Challenges for International Medical Graduate Radiologists
Bibliographic record
Abstract
The United States is facing shortages in a myriad of medical fields, including diagnostic radiology (DR). The increasing number of imaging studies, owing to advancing technology and an aging population, is outgrowing the capacity of radiologists. However, there have not been effective long-term solutions to manage this issue, which could have a looming effect on patient care and radiology residents' education. The alternate pathway introduced by the American Board of Radiology (ABR) allows international medical graduates (IMGs) who have completed DR residency outside the U.S. and Canada to be eligible for board certification after 4 additional years of training in the U.S. This eligibility requirement can be met by a combination of fellowships, up to 3 years of residency, and/or faculty appointments at the same time. The criteria were updated in 2022, facilitating the certification process for applicants. The changes included completing the 4 years of training within 8 years instead of the preapproved 4 consecutive years, omitting the obligatory 4 months of nuclear radiology training, and the ability to retrospectively include 2 years of training before the date of application. Considering the lack of reliable, up-to-date data in the literature and to highlight the importance of this pathway for the radiology community, this special report discusses the contribution of IMGs to the different aspects of the health care system in the U.S., with a focus on DR, as well as the newly updated alternate pathway criteria. Potential critical logistic challenges the IMG applicants may face are reviewed. Finally, the report proposes actions that could facilitate the ABR certification process and help integrate these highly qualified IMG radiologists into the American health care system.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".