Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology
Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology
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DOI：
10.1148/radiol.2020201434
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发表时间：
2020-12-01
期刊：
影响因子：
19.7
通讯作者：
Dako, Farouk
中科院分区：
文献类型：
--
作者：
Mollura, Daniel J.;Culp, Melissa P.;Dako, Farouk
放射学资源的稀缺或缺乏阻碍了资源匮乏的医疗机构采用人工智能（AI）进行医学成像。他们面临着本地设备、人员专业知识、基础设施、数据权利框架和公共政策的限制。人工智能在全球健康和低资源环境中的医疗决策可信度受到数据多样性不足、人工智能算法不透明以及资源匮乏的卫生机构对人工智能生产和验证的参与有限的阻碍。RAD-AID提出了在资源匮乏的卫生机构采用AI的三管齐下的综合战略，其中包括临床放射学教育，基础设施实施和分阶段引入AI。这一战略源于RAD-AID作为一个非营利组织在美国和中低收入国家资源匮乏的卫生机构中发展放射学的十多年经验。这三个组成部分协同作用，为解决保健差距奠定了基础。通过全面的教育，增强当地放射学人员的专业知识。软件、硬件以及放射学和网络基础设施使放射学工作流程能够融入人工智能。这些教育和基础设施的发展是在RAD-AID通过全球卫生合作分阶段引入、测试和扩展人工智能的同时发生的。(C)RSNA，2020年
Scarce or absent radiology resources impede adoption of artificial intelligence (AI) for medical imaging by resource-poor health institutions. They face limitations in local equipment, personnel expertise, infrastructure, data-rights frameworks, and public policies. The trustworthiness of AI for medical decision making in global health and low-resource settings is hampered by insufficient data diversity, nontransparent AI algorithms, and resource-poor health institutions' limited participation in AI production and validation. RAD-AID's three-pronged integrated strategy for AI adoption in resource-poor health institutions is presented, which includes clinical radiology education, infrastructure implementation, and phased AI introduction. This strategy derives from RAD-AID's more-than-a-decade experience as a nonprofit organization developing radiology in resource-poor health institutions, both in the United States and in low- and middle-income countries. The three components synergistically provide the foundation to address health care disparities. Local radiology personnel expertise is augmented through comprehensive education. Software, hardware, and radiologic and networking infrastructure enables radiology workflows incorporating AI. These educational and infrastructure developments occur while RAD-AID delivers phased introduction, testing, and scaling of AI via global health collaborations. (C) RSNA, 2020