Building BRIDGEs: Coordinating Standards, Diversity, and Ethics to Advance Biomedical AI

美国国立卫生研究院的数据科学战略计划提出了一个宏伟而雄心勃勃的目标，以提高数据科学的多样性。 数据科学劳动力和参与代表性不足的少数民族社区。事实上，一个道德的愿景 现代AI/ML医疗创新支持的公平社会只有在我们的利益- 持有人包括所有社区的代表。与NIH的多样性目标同步，该应用程序 旨在通过关键的领导团队在生物医学AI/ML社区中实施持久的变革， 我们的使命是培养多样化的下一代多学科医生和投资群体， 他们是实现Bridge 2AI的驱动力。简而言之，我们认识到生物医学领域的一个重大挑战， AI/ML教育限制了现代AI/ML在医疗保健和生物医学创新中的广泛采用。 在我们的劳动力中，普遍缺乏对道德和值得信赖的人工智能（ETAI）的理解， 与公众对使用AI/ML应用程序的焦虑相结合。这些担忧扼杀了潜在的影响 这些AI/ML技术。因此，我们建议设立Bridge 2AI-ENABLE学者奖。 成功实施AI/ML战略的关键是医学中出现的代表性不足 （URiM）;这些专业人员（例如，医学生，临床研究员，护士，医生）在他们的 医学专业，并致力于接受AI/ML的进一步培训，并推进临床实践。我们 拟议的Bridge 2AI-ENABLE学者奖将为多达15名URiM医学专业人员提供有针对性的AI/ML培训- 这些人有望成为未来的领导者，推动医疗保健领域的AI/ML创新。URiM浓缩 活动将包括由临床医生、AI/ML专家和ETAI领导人组成的指定导师团队。我们 已经组织了一个全面的10周长的培训计划，其中包括：1）经过深思熟虑的申请人重新- 导师计划和学员任命协议; 2）来自加州大学洛杉矶分校学校的坚定承诺的导师团队 医学，医学信息学和计算医学系遵循我们的导师选择过程; 以及3）定制课程，使每个学员都能完成培训。总体而言，我们的培训平台 旨在克服围绕生物医学AI/ML的焦虑，建立社区信任，并使学员能够探索 生物医学AI/ML主题，从零开始。每个学员都可以设计自己的个性化课程， 以自己的速度完成AI教育。大部分课程通过电子学习平台进行， 并辅以一对一的导师辅导和问答咨询时间。每名学员将与社署核心小组合作， 定制培训计划并设计个性化课程，使他们能够完成AI教育- 在他们自己的空间中进行阳离子，并在现实世界的场景中进行AI/ML项目。导师团队将 负责为每个学员创建里程碑报告，以引导成功的职业轨迹。的目标 这项补充活动是为了支持URiM医疗专业人员获得必要的理解， AI/ML技能，使他们成为推动现代医疗保健AI战略的驱动力。

The NIH's Strategic Plan for Data Science sets forth a grand and ambitious goal to enhance the diversity of the data science workforce and to engage underrepresented minority communities. Indeed, the vision of an ethical and equitable society supported by modern AI/ML healthcare innovations can only be achieved when our stake- holders include representatives of all communities. In lockstep with the NIH's diversity goals, this application aims to enact long-lasting change in the biomedical AI/ML community by a key leadership workforce to advance our mission in fostering the growth of a diverse next-generation multidisciplinary cohort of physicians and inves- tigators who are the driving force enabling Bridge2AI. Briefly, we recognize a major challenge in biomedical AI/ML education that limits the widespread adoption of modern AI/ML in health care and biomedical innovations. There exists a general lack of understanding regarding ethical and trustworthy AI (ETAI) in our workforce, com- bined with public anxiety surrounding the use of AI/ML applications. These concerns stifle the potential impact of these AI/ML technologies. Accordingly, we propose to establish the Bridge2AI-ENABLE Scholar Award. Essential to the successful implementation of AI/ML strategies are the emerging underrepresented in medicine (URiM); these professionals (e.g., medical students, clinical fellows, nurses, physicians) are well-trained in their medical professions and are committed to undergo further training in AI/ML and to advance clinical practice. Our proposed Bridge2AI-ENABLE Scholar Award will offer targeted AI/ML training for up to 15 URiM medical profes- sionals who are poised to become future leaders driving AI/ML innovation in health care. This URiM enrichment activity will consist of designated mentor teams composed of clinicians, AI/ML specialists, and ETAI leaders. We have organized a comprehensive 10-week long training plan that includes: 1) a well-thought-out applicant re- cruitment plan and mentee appointment protocol; 2) a mentor team with strong commitment from UCLA School of Medicine, medical informatics, and computational medicine faculty that follow our mentor selection processes; and 3) a customized curriculum tailoring each mentee to completing their training. Overall, our training platform aims to overcome anxiety surrounding biomedical AI/ML, build community trust, and empower trainees exploring biomedical AI/ML topics with entry at ground zero. Each trainee can design their personalized curriculum and complete AI education at their own pace. The majority of the curriculum is conducted via an e-learning platform, complemented by 1-on-1 mentorship and A&Q consultation hours. Each mentee will partner with the SWD Core to customize training plans and design their personalized curriculum, which enables them to complete AI edu- cation in their own space and to carry out AI/ML projects in real-world scenarios. The mentor teams will be responsible for creating milestone reports for each mentee to steer a successful career trajectory. The goal of this supplemental activity is to support URiM medical professionals to acquire necessary understanding and skills in AI/ML and to enable them to be the driving force to advance AI strategies in modern healthcare.