arXiv cs.AI· Raffaele Giancotti, Francesca Santucci, Amama Anjum, Monique de Villa, Rupsa Mitra, Soham Biren Katlariwala, Vishala Mishra, Leo Anthony Celi·· 3 小时前AI 评分35
研究分析美国健康 AI 教育项目课程中的数据来源与伦理教学
Data Provenance and Ethics in the Curricula of U.S. Health AI Education Programs
AI 导读
一项横断面研究分析了 2026 年 4 月至 7 月检索到的美国 21 所大学 36 个健康 AI 教育项目的公开课程。80.6% 的项目涵盖伦理，但仅 22.2% 教授数据来源，16.7% 涉及结构性来源，7 个硕士项目无一达标，也没有项目涉及计算的环境成本。92 名授课教师中 76.1% 来自计算学科，仅 3.3% 来自社会科学或人文学科。
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