数据释放和医疗研究中的众包:集体和人工智能的交集
Jefferson R Wilson1, Luciano M Prevedello1, Christopher D Witiw1
1From the Division of Neurosurgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), St Michael's Hospital, 30 Bond St, Toronto, ON, Canada M5B 1W8; Department of Surgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), University of Toronto, Toronto, Canada (J.R.W., C.D.W.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.).
Radiology. Artificial intelligence
|January 17, 2024
概括
数据解放和众包可以通过克服数据孤岛来加速医疗人工智能研究. 这些方法为创新提供了好处,但需要仔细考虑潜在的挑战.
科学领域:
- 医疗数据分析 医学数据分析
- 医疗保健中的人工智能
背景情况:
- 全球医疗数据的指数增长.
- 医疗数据的显著难以访问,用于高级分析.
- 数据孤岛阻碍了人工智能驱动的医疗保健解决方案的发展.
研究的目的:
- 检查医疗人工智能研究中的数据释放和众包.
- 总结这些数据访问方法的好处和缺点.
- 倡导扩大使用数据释放和众包.
主要方法:
- 对数据释放和众包的概念性检查.
- 分析它们在医学AI中的应用.
- 包括一个实用的众包竞争示例,使用医学成像数据.
主要成果:
- 数据解放和众包可以弥合数据孤岛.
- 这些方法加速了医疗人工智能的国际创新.
- 一个众包竞赛证明了实际应用.
结论:
- 建议扩大使用数据释放和众包.
- 这些方法对于推动医疗人工智能研究至关重要.
- 解决潜在的陷对于成功实施至关重要.
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