Geographic prompting and content fidelity in generative Artificial Intelligence: A multi-model study of demographics and imaging equipment in AI-generated videos and images of Canadian medical radiation technologists
Bibliographic record
Abstract
BACKGROUND: As generative AI tools increasingly produce medical imagery and videos for education, marketing, and communication, concerns have arisen about the accuracy and equity of these representations. Existing research has identified demographic biases in AI-generated depictions of healthcare professionals, but little is known about their portrayal of Medical Radiation Technologists (MRTs), particularly in the Canadian context. METHODS: This study evaluated 690 AI-generated outputs (600 images and 90 videos) created by eight leading text-to-image and text-to-video models using the prompt ``Image [or video] of a Canadian Medical Radiation Technologist.'' Each image and video was assessed for demographic characteristics (gender, race/ethnicity, age, religious representation, visible disabilities), and the presence and accuracy of imaging equipment. These were compared to real-world demographic data on Canadian MRTs (n = 20,755). RESULTS: Significant demographic discrepancies were observed between AI-generated content and real-world data. AI depictions included a higher proportion of visible minorities (as defined by Statistics Canada) (39% vs. 20.8%, p < 0.001) and males (41.4% vs. 21.2%, p < 0.001), while underrepresenting women (58.5% vs. 78.8%, p < 0.001). Age representation skewed younger than actual workforce demographics (p < 0.001). Equipment representation was inconsistent, with 66% of outputs showing CT/MRI and only 4.3% showing X-rays; 26% included inaccurate or fictional equipment. CONCLUSION: Generative AI models frequently produce demographically and contextually inaccurate depictions of MRTs, misrepresenting workforce diversity and clinical tools. These inconsistencies pose risks for educational accuracy, public perception, and equity in professional representation. Improved model training and prompt sensitivity are needed to ensure reliable and inclusive AI-generated medical content.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".