Artificial Intelligence-Generated Facial Images for Medical Education

被引：0

作者：

Bingwen Eugene Fan

Minyang Chow

Stefan Winkler

机构：

[1] Centre for Healthcare Innovation,Department of Haematology

[2] Tan Tock Seng Hospital,Lee Kong Chian School of Medicine

[3] Nanyang Technological University,Department of General Medicine

[4] Yong Loo Lin School of Medicine,School of Computing

[5] National University of Singapore,undefined

[6] Tan Tock Seng Hospital,undefined

[7] Massachussets General Hospital Institute of Health Professions,undefined

[8] Harvard Macy Institute,undefined

[9] ASUS Intelligent Cloud Services (AICS),undefined

[10] National University of Singapore,undefined

来源：

Medical Science Educator | 2024年 / 34卷

关键词：

Artificial intelligence; Text-to-image models; Medical education; Patient privacy;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

We evaluated the use of text-to-image models (Microsoft’s Bing Image creator (powered by DALL·E) and Shutterstock’s AI image generator) to generate realistic images of human faces and their associated pathology, which may be useful for medical education, given they may overcome issues of patient privacy and requirement for consent. These models have potential to augment rare medical image datasets for medical education, as well as provide greater inclusivity and representation of diverse populations.

引用

页码：5 / 7

页数：2

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