在放射学人工智能领域构建多样性、公平性和包容性：代表性至关重要，从数据到劳动力。

Building Diversity, Equity, and Inclusion Within Radiology Artificial Intelligence: Representation Matters, From Data to the Workforce.

作者信息

Doo Florence X, McGinty Geraldine B

机构信息

Director of Innovation, University of Maryland Medical Intelligent Imaging Center (UM2ii), Baltimore, Maryland; Member, Committee on Economics in Academic Radiology, under the ACR Commission on Economics.

Senior Associate Dean for Clinical Affairs, Professor of Clinical Radiology and Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, New York; Founder, RADEqual; Chair, International Society of Radiology Commission on Education. Electronic address: https://twitter.com/DrGMcGinty.

出版信息

J Am Coll Radiol. 2023 Sep;20(9):852-856. doi: 10.1016/j.jacr.2023.06.014. Epub 2023 Jul 14.

DOI:10.1016/j.jacr.2023.06.014

PMID:37453602

Abstract

Diversity, equity, and inclusion (DEI) is both a critical ingredient and moral imperative in shaping the future of radiology artificial intelligence (AI) for improved patient care, from design to deployment. At the design level: Potential biases and discrimination within data sets results in inaccurate radiology AI models, and there is an urgent need to purposefully embed DEI principles throughout the AI development and implementation process. At the deployment level: Diverse representation in radiology AI leadership, research, and career development is necessary to avoid worsening structural and historical health inequities. To create an inclusive and equitable AI-enabled future in healthcare, a DEI radiology AI leadership training program may be needed to cultivate a diverse and sustainable pipeline of leaders in the field.

摘要

多元化、公平性和包容性（DEI）对于塑造放射学人工智能（AI）的未来以改善患者护理而言，既是关键要素，也是道德要求，贯穿从设计到部署的全过程。在设计层面：数据集中潜在的偏差和歧视会导致放射学AI模型不准确，因此迫切需要在AI开发和实施过程中有意融入DEI原则。在部署层面：放射学AI领域的领导层、研究和职业发展需要有多元化的代表，以避免加剧结构性和历史性的健康不平等。为了在医疗保健领域创造一个包容且公平的人工智能未来，可能需要一个DEI放射学AI领导力培训项目，以培养该领域多元化且可持续的领导人才队伍。