Cancer Research Workforce Development in FAIR Artificial Intelligence and Machine Learning

鉴于可用于癌症和其他疾病的临床和分子数据的收集迅速增长 疾病、人工智能(AI)和机器学习(ML)的潜在应用 应用更好和更具成本效益的方法进行临床决策是引人注目的。 然而，AI/ML算法的成功应用，特别是在临床领域， 这取决于AI/ML模型训练和验证所需数据的可用性和质量。 因此，一个未得到满足的需求是发展所需的能力和技能 生物医学数据可用于满足以下四个公平要求的AI/ML应用： 可查找、可访问、可互操作和可重复使用。这是我们综合节目的补充内容 在癌症数据科学中通过开发一个公平应用的短期课程来满足这一需求 将在三个地点分发：1)为博士生提供的短期课程通过 南佛罗里达大学，我们的癌症生物学博士项目的主办方 为莫菲特癌症博士后研究员和其他早期研究人员举办的讲习班 中心，以及3)通过莫菲特YouTube频道公开传播讲座录像。

Given the rapidly growing collection of clinical and molecular data available for cancer and other diseases, the potential application of artificial intelligence (AI) and machine learning (ML) approaches applied better and more cost-effective clinical decision making is compelling. However, the success of the application of AI/ML algorithms, especially in the clinical domain, hinges on the availability and quality of data for training and validation of the AI/ML models. Therefore, an unmet need is in the development of competencies and skills needed to make biomedical data ready for AI/ML applications that can meet the four FAIR requirements: Findable, Accessible, Interoperable, and Reusable. This supplement to our Integrated Program in Cancer Data Science addresses this need by developing a short course in FAIR application that will be distributed in three venues: 1) a short-course for Ph.D student offer through the University of South Florida, which host our Cancer Biology PhD Program, 2) hands-on workshops for postdoctoral fellows and other early-staged investigator at the Moffitt Cancer Center, and 3) public dissemination of videotaped lectures via Moffitt YouTube channel.