Department of Family Medicine, McGill University, Montreal, QC, Canada.
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada.
J Med Internet Res. 2024 Nov 15;26:e63356. doi: 10.2196/63356.
Recent studies have identified significant gaps in equity, diversity, and inclusion (EDI) considerations within the lifecycle of artificial intelligence (AI), spanning from data collection and problem definition to implementation stages. Despite the recognized need for integrating EDI principles, there is currently no existing guideline or framework to support this integration in the AI lifecycle.
This study aimed to address this gap by identifying EDI principles and indicators to be integrated into the AI lifecycle. The goal was to develop a comprehensive guiding framework to guide the development and implementation of future AI systems.
This study was conducted in 3 phases. In phase 1, a comprehensive systematic scoping review explored how EDI principles have been integrated into AI in health and oral health care settings. In phase 2, a multidisciplinary team was established, and two 2-day, in-person international workshops with over 60 representatives from diverse backgrounds, expertise, and communities were conducted. The workshops included plenary presentations, round table discussions, and focused group discussions. In phase 3, based on the workshops' insights, the EDAI framework was developed and refined through iterative feedback from participants. The results of the initial systematic scoping review have been published separately, and this paper focuses on subsequent phases of the project, which is related to framework development.
In this study, we developed the EDAI framework, a comprehensive guideline that integrates EDI principles and indicators throughout the entire AI lifecycle. This framework addresses existing gaps at various stages, from data collection to implementation, and focuses on individual, organizational, and systemic levels. Additionally, we identified both the facilitators and barriers to integrating EDI within the AI lifecycle in health and oral health care.
The developed EDAI framework provides a comprehensive, actionable guideline for integrating EDI principles into AI development and deployment. By facilitating the systematic incorporation of these principles, the framework supports the creation and implementation of AI systems that are not only technologically advanced but also sensitive to EDI principles.
最近的研究表明，人工智能（AI）生命周期中存在着重大的公平、多样性和包容性（EDI）考虑因素的差距，从数据收集和问题定义到实施阶段都存在这些问题。尽管人们认识到需要整合 EDI 原则，但目前还没有现有的准则或框架来支持人工智能生命周期中的这种整合。
本研究旨在通过确定要纳入人工智能生命周期的 EDI 原则和指标来解决这一差距。目标是制定一个全面的指导框架，指导未来 AI 系统的开发和实施。
本研究分 3 个阶段进行。在第 1 阶段，进行了全面的系统范围的综述，以探讨 EDI 原则如何在健康和口腔保健领域的 AI 中得到整合。在第 2 阶段，成立了一个多学科团队，并举办了两次为期两天的面对面国际研讨会，有来自不同背景、专业知识和社区的 60 多名代表参加。研讨会包括全体会议、圆桌讨论和重点小组讨论。在第 3 阶段，根据研讨会的见解，通过参与者的迭代反馈，开发和完善了 EDAI 框架。最初的系统范围综述的结果已单独发表，本文重点介绍了与框架开发相关的项目的后续阶段。
在本研究中，我们开发了 EDAI 框架，这是一个贯穿整个 AI 生命周期整合 EDI 原则和指标的全面指南。该框架解决了从数据收集到实施各个阶段存在的差距，并侧重于个人、组织和系统层面。此外，我们还确定了在健康和口腔保健中整合 AI 生命周期内 EDI 的促进因素和障碍。
所开发的 EDAI 框架为将 EDI 原则纳入 AI 开发和部署提供了一个全面、可操作的指南。通过系统地纳入这些原则，该框架支持创建和实施不仅技术先进而且对 EDI 原则敏感的 AI 系统。
