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
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.
Authors
- Yousif Al-Naser (ORCID: https://orcid.org/0000-0002-2954-0954)
- Sonali Sharma (ORCID: https://orcid.org/0000-0003-3803-0507)
- Faisal Khosa (ORCID: https://orcid.org/0000-0001-5681-7683)
- Charlotte J. Yong‐Hing (ORCID: https://orcid.org/0000-0001-5014-1651)
- Kevin Ibach
- Ken Niure
Institutions
- BC Cancer Agency (CA)
- University of British Columbia (CA)
- Trillium Health Centre (CA)
- McMaster University Medical Centre (CA)
- Spinal Cord Injury BC (CA)
Publication Details
- Journal
- Journal of medical imaging and radiation sciences
- Published
- 2025-10-18
- DOI
- https://doi.org/10.1016/j.jmir.2025.102122
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00