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: 10473397
• 负责人: ALEX BUI
• 金额: $ 250.42万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10473397
• 关键词: Accountability; Advocate; Area; Artificial Intelligence; Behavioral; Behavioral Research; Benchmarking; Biomedical Research; Bridge to Artificial Intelligence; California; Caring; Collaborations; Communication; Communication Methods; Communities; Data; Data Collection; Data Science; Data Set; Development; Discipline; Education; Electronic Health Record; Elements; Ensure; Equilibrium; Ethics; Evaluation; FAIR principles; Feedback; Florida; Fostering; Future; Generations; Goals; Growth; Health; Health Sciences; Healthcare; Heterogeneity; Image; Infrastructure; Institution; Leadership; Learning; Legal; Life; Marshal; Measures; Methods; Michigan; Mission; Morphologic artifacts; Oregon; Outcome; Participant; Policies; Privacy; Process; Productivity; Publishing; Records; Reporting; Research; Science; Shapes; Source; Sum; Techniques; Touch sensation; Training; United States National Institutes of Health; Universities; Vision; Work; Workforce Development; base; biomedical informatics; cohesion; design; effectiveness evaluation; experience; health care delivery; improved; innovation; insight; interest; mHealth; meetings; next generation; novel; programs; rapid technique; skill acquisition; skills; social; success; synergism; tool; tool development; trend; trustworthiness

OVERALL: ABSTRACT (PROJECT DESCRIPTION) Bridge2AI is a signature NIH initiative. It recognizes the challenges and opportunities in the growth of data sci- ence and data-driven methods for biomedical and behavioral research and healthcare delivery. We have reached a key moment: with the exponential growth of our ability to collect and analyze data, we must consider how we use this information to benefit everyone in an equitable way, providing a collective path forward. Data Generation Projects (DGPs) within Bridge2AI will tackle “grand challenges”: questions that will shape future scientific dis- covery and can ultimately impact the health and care of many. Marshalling these forces collectively requires experience and insight to create a collaborative, interdisciplinary endeavor that brings together disparate stake- holders to realize Bridge2AI’s mission: discovery, collaboration, and learning. Building from our collective experience in successfully guiding large NIH initiatives and (inter)national scientific consortia, our BRIDGE Coordination Center (CC) is designed to ensure a responsive set of Cores that will sup- port and enable the DGPs in their grand challenges. Representing multiple institutions (UCLA, Penn State Uni- versity, University of Florida, University of Michigan, University of Southern California, Oregon Health & Sciences University, Sage Bionetworks, EMBL-EBI), we propose multiple interacting Cores. These Cores have interdisci- plinary expertise across several key areas, including biomedical informatics/data science and AI (methods, ap- plications, evaluation), as well as across different domains and data types. Our Cores (Ethics, Standards, Tool Optimization, Skills & Workforce Development) are ready to interact to facilitate cross-cutting activities related to ethics and trustworthy artificial intelligence (ETAI); FAIR principles (findable, accessible, interoperable, reusable) across emergent datasets and domains; comparison and benchmarking of developed AI-ready datasets and tools. Across our CC we will create a basis for diverse trainees to not only appreciate the implications of AI in biomedical/behavioral research, but to meaningfully engage with them – embracing the heterogeneity of experi- ences, backgrounds, and objectives to maximize the richness and strength this diversity brings in our actions. We plan to work with a Teaming Core to enable activities that bring together disparate groups within Bridge2AI. Our efforts are organized by a skilled Administrative Core who will provide oversight and cohesion to this en- deavor, both across the Cores as well as with the DGPs and NIH. Our Cores are shaped to maximize the inte- gration and sharing of ideas across the DGPs and Bridge2AI as a whole through dynamic, contemporary com- munication methods; the refinement and dissemination of best practices between these groups and wider sci- entific community through multiple venues; and the evaluation of the effectiveness of the methods and overall Bridge2AI initiative. This CC will provide a unified framework for Bridge2AI to engage and education different stakeholders, and together blaze a collective trail forward for biomedical and behavioral AI – for everyone.

总体：摘要（项目描述） Bridge2AI 是 NIH 的一项标志性举措，它认识到数据科学发展中的挑战和机遇。 我们已经实现了生物医学和行为研究以及医疗保健服务的科学和数据驱动方法。 关键时刻：随着我们收集和分析数据的能力呈指数级增长，我们必须考虑如何 使用这些信息以公平的方式使每个人受益，提供集体数据生成的道路。 Bridge2AI 内的项目 (DGP) 将解决“重大挑战”：将影响未来科学发展的问题 覆盖范围并最终会影响许多人的健康和护理需要共同调动这些力量。 经验和洞察力创造一个协作的、跨学科的努力，将不同的利益相关者聚集在一起 持有人实现 Bridge2AI 的使命：发现、协作和学习。 借鉴我们成功指导大型 NIH 计划和（国际）国家科学的集体经验 联盟，我们的 BRIDGE 协调中心 (CC) 旨在确保一组响应迅速的核心，以支持 代表多个机构（加州大学洛杉矶分校、宾夕法尼亚州立大学）并帮助 DGP 应对重大挑战。 佛罗里达大学、密歇根大学、南加州大学、俄勒冈州健康与科学大学 大学、Sage Bionetworks、EMBL-EBI），我们提出了多个相互作用的核心，这些核心具有跨学科性。 跨几个关键领域的专业知识，包括生物医学信息学/数据科学和人工智能（方法、应用程序） 我们的核心（道德、标准、工具），以及跨不同领域和数据类型的核心。 优化、技能和劳动力发展）已准备好进行互动，以促进与以下方面相关的跨领域活动 道德和值得信赖的人工智能（ETAI）；公平原则（可查找、可访问、可互操作、可重用） 跨新兴数据集和领域；对已开发的人工智能就绪数据集进行比较和基准测试 在我们的 CC 中，我们将为不同的学员奠定基础，让他们不仅能够了解人工智能在行业中的影响。 生物医学/行为研究，但要有意义地参与其中——拥抱经验的异质性 的背景和目标，以最大限度地发挥这种多样性给我们的行动带来的丰富性和力量。 我们计划与 Teaming Core 合作，开展将 Bridge2AI 内不同群体聚集在一起的活动。 我们的工作是由熟练的行政核心组织的，他们将为这一项目提供监督和凝聚力。 核心以及 DGP 和 NIH 的努力，我们的核心旨在最大限度地提高内部的互动。 通过动态的、当代的合作，DGP 和 Bridge2AI 作为一个整体来交流和分享想法。 交流方法；在这些群体和更广泛的科学界之间完善和传播最佳实践 通过多个场所建立实体社区；以及评估方法和整体的有效性； Bridge2AI 计划将为 Bridge2AI 提供一个统一的框架来吸引和教育不同的人。 利益相关者，共同为每个人的生物医学和行为人工智能开辟一条集体道路。