促进人工智能和机器学习教育的多学科合作:基于AI-READI训练营的教程
Taiki W Nishihara1, Fritz Gerald P Kalaw1,2, Adelle Engmann3
1Viterbi Family Department of Ophthalmology and Shiley Eye Institute, Hamilton Glaucoma Center, Division of Ophthalmology Informatics and Data Science, University of California, San Diego, 9415 Campus Point Drive, La Jolla, CA, 92093, United States, 1 858-534-8413.
JMIR medical education
|December 29, 2025
概括
在AI-READI训练营成功地培训生物医学专业人员在人工智能 (AI) 和机器学习 (ML) 使用现实世界的数据. 该计划弥合了未来健康创新的临床和计算专业知识之间的差距.
科学领域:
- 生物医学研究的研究.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 生物医学研究中AI/ML的跨学科培训有限,这在数据科学家和临床医生之间造成了差距.
- 美国国立卫生研究院的Bridge2AI倡议启动了AI-READI,以创建用于糖尿病研究的多式联络,公平的数据集.
- AI-READI旨在培养熟练掌握计算方法和临床应用的劳动力.
研究的目的:
- 描述AI-READI训练营的设计,改进和结果.
- 分享学习经验,以开发未来的生物医学研究多学科AI/ML培训计划.
主要方法:
- 一个80小时的训练营,结合讲座,编码和指导,每年根据参与者的反改进.
- 第一年专注于基础的Python和ML技术;第二年整合了AI-READI数据集,并增加了大语言模型和FAIR数据原则的模块.
- 参与者的满意度和特征通过前后调查进行评估,并以主题分析定性反.
主要成果:
- 在这两年报告的参与者满意度都很高,第二年由于小队伍和应用学习而有所改善.
- 第二年在教师效率,员工支持和整体享受方面取得了完美的成绩.
- 参与者重视使用多式联络生物医学数据集的工作,同行协作,以及学习技能的适用性.
结论:
- 人工智能-READI训练营展示了一个成功的模型,通过反驱动的多学科培训,在生物医学人工智能领域建立技术和临床专业知识的桥梁.
- 关键要素包括多元化的队列,使用相关数据集的应用学习和持续的指导.
- 未来的代将包括启动营前的模块,客观技能评估和研究生产力的长期跟踪.
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