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

搭建桥梁：协调标准、多样性和道德以推进生物医学人工智能

• 批准号: 10801686
• 负责人: ALEX BUI
• 金额: $ 58.6万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-06 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10801686
• 关键词: Adoption; Anxiety; Appointment; Automobile Driving; Award; Bridge to Artificial Intelligence; Clinical; Communities; Complement component C1; Consultations; Data Science; E-learning; Education; Educational Curriculum; Equity; Ethics; Faculty; Fostering; Future; Goals; Growth; Healthcare; Hour; Leadership; Medical; Medical Informatics; Medical Students; Medicine; Mentors; Mentorship; Mission; Modernization; Nurses; Physicians; Process; Protocols documentation; Reporting; Research Personnel; Societies; Specialist; Strategic Planning; Technology; Training; Trust; Underrepresented Minority; United States National Institutes of Health; Vision; career; clinical care; clinical practice; cohort; community building; design; diverse data; driving force; empowerment; experience; innovation; medical schools; minority communities; multidisciplinary; next generation; programs; recruit; skills; trustworthiness

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.

美国国家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，以增强数据的多样性 数据科学工作人员，并与代表不足的少数族裔社区接触。事实上，一种道德的愿景 由现代AI/ML医疗创新支持的公平社会只有在我们的利益- 持有者包括所有社区的代表。与美国国立卫生研究院的多样性目标同步，这项申请 旨在通过关键的领导团队推动生物医学AI/ML社区的长期变革 我们的使命是促进多元化的下一代多学科医生队伍的发展，并投资- 老虎是使Bridge2AI成为可能的驱动力。简而言之，我们认识到生物医学领域的一个主要挑战 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。 在我们的员工队伍中，普遍缺乏对道德和值得信赖的人工智能(ETAI)的理解。 伴随着公众对AI/ML应用程序使用的焦虑。这些担忧扼杀了潜在的影响 在这些AI/ML技术中。因此，我们建议设立Bridge2AI-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 并致力于接受AI/ML方面的进一步培训，并推动临床实践。我们的 拟议的Bridge2AI-Enable学者奖将为多达15名Urim医学教授提供有针对性的AI/ML培训- 准备成为推动医疗保健领域AI/ML创新的未来领导者的专业人士。这次乌里姆浓缩 活动将由指定的导师团队组成，由临床医生、AI/ML专家和ETAI领导者组成。我们 组织了为期10周的全面培训计划，其中包括：1)经过深思熟虑的申请者重新 巡回计划和学员预约协议；2)来自加州大学洛杉矶分校的坚定承诺的导师团队 医学、医学信息学和计算医学学院遵循我们的导师选择程序； 3)定制课程，为每个学员量身定做，以完成培训。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，从零开始进入。每个学员都可以设计自己的个性化课程， 以自己的节奏完成AI教育。大部分课程是通过电子学习平台进行的， 辅以一对一辅导和问答咨询时间。每名学员将与社署核心合作 定制培训计划，设计个性化课程，使他们能够完成人工智能教育。 阳离子在他们自己的空间中，并在真实世界的场景中开展AI/ML项目。指导团队将会是 负责为每位学员创建里程碑式的报告，引导他们走上成功的职业发展轨道。的目标是 这项补充活动是为了支持URIM医疗专业人员获得必要的了解和 在人工智能/ML方面的技能，并使他们能够成为推动现代医疗保健中人工智能战略的推动力。