arXiv cs.AI· Raffaele Giancotti, Francesca Santucci, Amama Anjum, Monique de Villa, Rupsa Mitra, Soham Biren Katlariwala, Vishala Mishra, Leo Anthony Celi·· 14 小时前AI 评分31
美国健康 AI 教育课程中的数据溯源与伦理覆盖分析
Data Provenance and Ethics in the Curricula of U.S. Health AI Education Programs
AI 导读
一项针对美国 21 所大学 36 个健康 AI 教育项目的横断面分析发现，80.6% 的项目讲授伦理，但仅 22.2% 涉及数据溯源、16.7% 覆盖结构性溯源，7 个硕士项目无一达到结构性或数据集局限层面的溯源覆盖标准。所有项目均未涉及算力环境成本，18 个项目含团队协作但仅 5 个跨临床与技术背景组队；92 名具名讲师中 76.1% 来自计算学科，可判定性别的 51 人中 66.7% 为男性。
正文 · 原文
Abstract:Failures of deployed clinical artificial intelligence (AI) systems have often originated in how their training data were generated. Whether the programs that train the health AI workforce teach practitioners to examine data provenance is unknown. We conducted a cross-sectional analysis of the publicly advertised curricula of health AI education programs at U.S. universities, identified through a structured multisource web search between April and July 2026. Two investigators coded coverage of data provenance (structural, dataset limitations, or data management only), ethics, and sustainability; collaborative structure; technical prerequisites; and the disciplinary background and gender of named instructors. The primary outcome was provenance coverage at the structural or dataset-limitations level. Of 43 candidate entries, 36 programs at 25 institutional units in 21 universities were included. Ethics was covered by 29 programs (80.6%; 95% confidence interval [CI], 65.0 to 90.2), data provenance by 8 (22.2%; 95% CI, 11.7 to 38.1), and structural provenance by 6 (16.7%; 95% CI, 7.9 to 31.9). None of the seven master's degree programs met the primary outcome, and no program addressed sustainability. Eighteen programs included team-based work, but only 5 assembled teams across clinical and technical backgrounds. Of 92 named instructors, 76.1% came from computational disciplines and 3.3% from the social sciences or humanities; among the 51 whose gender could be determined, 66.7% were men. U.S. health AI education programs widely taught ethics but rarely taught how clinical data are produced. None addressed the environmental cost of computation, and most instructors came from computational disciplines.
| Comments: | 10 pages, 1 figure, 3 tables. Under review at PLOS Digital Health. Data and supplementary material available at this https URL |
| Subjects: | Computers and Society (cs.CY); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39606 [cs.CY] |
| (or arXiv:2610.39606v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39606 arXiv-issued DOI via DataCite |
Submission history
From: Raffaele Giancotti Dr. [view email]
[v1]
Thu, 29 Oct 2026 08:41:46 UTC (385 KB)
来源：arXiv cs.AI · arxiv.org