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。

IF 3.7 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Yousif Al-Naser, Sonali Sharma, Ken Niure, Kevin Ibach, Charlotte J Yong-Hing
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引用次数: 0

摘要

基本原理和目标:人工智能(AI)工具,特别是生成模型,越来越多地用于描述医疗保健中的临床角色。本研究评估了生成式人工智能系统是否能准确区分放射科医生和医疗放射技术专家(mrt),这两个角色经常被患者和提供者混淆。材料和方法:我们评估了由8个文本到图像/视频人工智能模型生成的1380张图像和视频。五名评分员评估任务角色的准确性、着装、设备、照明、隔离和人口统计。统计测试比较了模型和角色之间的差异。结果:在82.0%的输出中,mrt被准确描述,而只有56.2%的放射科医生图像/视频是角色合适的。在不准确的放射科医生描述中,79.1%错误地描述了mrt任务。放射科医生多为男性(73.8%)和白人(79.7%),而mrt则更为多样化。听诊器误用、缺乏残疾/宗教标志、放射科医生过度穿着职业装进一步反映了偏见。结论:生成式人工智能经常歪曲放射科医生的角色和人口统计学,强化刻板印象和公众困惑。需要加强监督和纳入标准,以确保公平的人工智能生成的医疗保健内容。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
CiteScore
6.20
自引率
12.90%
发文量
98
审稿时长
6-12 weeks
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.
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