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 用于生物医学和行为研究以及医疗保健服务的数据驱动方法。我们已经达到了 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 利用这些信息以公平的方式使每个人受益，提供集体前进的道路。数据生成 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 覆盖并最终会影响许多人的健康和护理。将这些力量集中起来需要 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 端口并帮助 DGP 应对重大挑战。代表多个机构（加州大学洛杉矶分校、宾夕法尼亚州立大学 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。这些核心有跨学科 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） 复制、评估），以及跨不同领域和数据类型。我们的核心（道德、标准、工具 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；开发的人工智能就绪数据集的比较和基准测试 工具。在我们的 CC 中，我们将为不同的学员奠定基础，让他们不仅能够理解人工智能在 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。 各个核心以及 DGP 和 NIH 之间的合作。我们的核心的形状是为了最大限度地提高集成度 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。 通讯方法；这些团体和更广泛的科学界之间的最佳实践的完善和传播 通过多个场所形成实体社区；以及方法和总体有效性的评估 Bridge2AI 倡议。该 CC 将为 Bridge2AI 提供一个统一的框架，以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。