Yousif Al-Naser, Sonali Sharma, Ken Niure, Kevin Ibach, Charlotte J Yong-Hing
{"title":"放射学合成混淆:生成人工智能如何在面向患者的媒体中放大放射科医生和技术人员的误解。","authors":"Yousif Al-Naser, Sonali Sharma, Ken Niure, Kevin Ibach, Charlotte J Yong-Hing","doi":"10.1177/08465371251350085","DOIUrl":null,"url":null,"abstract":"<p><p><b>Rationale and Objectives:</b> Artificial intelligence (AI) tools, particularly generative models, are increasingly used to depict clinical roles in healthcare. This study evaluates whether generative AI systems accurately differentiate between radiologists and medical radiation technologists (MRTs), 2 roles often confused by patients and providers. <b>Materials and Methods:</b> We assessed 1380 images and videos generated by 8 text-to-image/video AI models. Five raters evaluated task-role accuracy, attire, equipment, lighting, isolation, and demographics. Statistical tests compared differences across models and roles. <b>Results:</b> MRTs were depicted accurately in 82.0% of outputs, while only 56.2% of radiologist images/videos were role-appropriate. Among inaccurate radiologist depictions, 79.1% misrepresented MRTs tasks. Radiologists were more often male (73.8%) and White (79.7%), while MRTs were more diverse. Stethoscope misuse, lack of disability/religious markers, and overuse of business attire for radiologists further reflected bias. <b>Conclusion:</b> Generative AI frequently misrepresents radiologist roles and demographics, reinforcing stereotypes and public confusion. Greater oversight and inclusion standards are needed to ensure equitable AI-generated healthcare content.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"8465371251350085"},"PeriodicalIF":3.7000,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Radiology Synthetic Confusion: How Generative Artificial Intelligence Amplifies Misunderstandings of Radiologists and Technologists in Patient-Facing Media.\",\"authors\":\"Yousif Al-Naser, Sonali Sharma, Ken Niure, Kevin Ibach, Charlotte J Yong-Hing\",\"doi\":\"10.1177/08465371251350085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b>Rationale and Objectives:</b> Artificial intelligence (AI) tools, particularly generative models, are increasingly used to depict clinical roles in healthcare. This study evaluates whether generative AI systems accurately differentiate between radiologists and medical radiation technologists (MRTs), 2 roles often confused by patients and providers. <b>Materials and Methods:</b> We assessed 1380 images and videos generated by 8 text-to-image/video AI models. Five raters evaluated task-role accuracy, attire, equipment, lighting, isolation, and demographics. Statistical tests compared differences across models and roles. <b>Results:</b> MRTs were depicted accurately in 82.0% of outputs, while only 56.2% of radiologist images/videos were role-appropriate. Among inaccurate radiologist depictions, 79.1% misrepresented MRTs tasks. Radiologists were more often male (73.8%) and White (79.7%), while MRTs were more diverse. Stethoscope misuse, lack of disability/religious markers, and overuse of business attire for radiologists further reflected bias. <b>Conclusion:</b> Generative AI frequently misrepresents radiologist roles and demographics, reinforcing stereotypes and public confusion. Greater oversight and inclusion standards are needed to ensure equitable AI-generated healthcare content.</p>\",\"PeriodicalId\":55290,\"journal\":{\"name\":\"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes\",\"volume\":\" \",\"pages\":\"8465371251350085\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2025-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/08465371251350085\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/08465371251350085","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Radiology Synthetic Confusion: How Generative Artificial Intelligence Amplifies Misunderstandings of Radiologists and Technologists in Patient-Facing Media.
Rationale and Objectives: Artificial intelligence (AI) tools, particularly generative models, are increasingly used to depict clinical roles in healthcare. This study evaluates whether generative AI systems accurately differentiate between radiologists and medical radiation technologists (MRTs), 2 roles often confused by patients and providers. Materials and Methods: We assessed 1380 images and videos generated by 8 text-to-image/video AI models. Five raters evaluated task-role accuracy, attire, equipment, lighting, isolation, and demographics. Statistical tests compared differences across models and roles. Results: MRTs were depicted accurately in 82.0% of outputs, while only 56.2% of radiologist images/videos were role-appropriate. Among inaccurate radiologist depictions, 79.1% misrepresented MRTs tasks. Radiologists were more often male (73.8%) and White (79.7%), while MRTs were more diverse. Stethoscope misuse, lack of disability/religious markers, and overuse of business attire for radiologists further reflected bias. Conclusion: Generative AI frequently misrepresents radiologist roles and demographics, reinforcing stereotypes and public confusion. Greater oversight and inclusion standards are needed to ensure equitable AI-generated healthcare content.
期刊介绍:
The Canadian Association of Radiologists Journal is a peer-reviewed, Medline-indexed publication that presents a broad scientific review of radiology in Canada. The Journal covers such topics as abdominal imaging, cardiovascular radiology, computed tomography, continuing professional development, education and training, gastrointestinal radiology, health policy and practice, magnetic resonance imaging, musculoskeletal radiology, neuroradiology, nuclear medicine, pediatric radiology, radiology history, radiology practice guidelines and advisories, thoracic and cardiac imaging, trauma and emergency room imaging, ultrasonography, and vascular and interventional radiology. Article types considered for publication include original research articles, critically appraised topics, review articles, guest editorials, pictorial essays, technical notes, and letter to the Editor.