生活在资源有限的环境中的人的健康需求是机器学习 （ML） 和医疗保健交汇领域中一个不容忽视和研究不足的部分。虽然近年来，随着深度学习的进步，ML在卫生保健中的使用得到了进一步的普及，但低收入和中等收入国家（LMIC）在过去十年中已经在卫生保健领域经历了自身的数字化转型，通过实现移动保健（mHealth），从而达成了跨越式发展。为了引入新技术，这些国家通常采用自上而下的方法重新开始，单独实施这些技术，结果导致缺少实际应用和资源浪费。在本文中，我们从当前研究差距，以及卫生保健专业人员在资源有限的环境中的真实经验两个角度，概述了必要的考量。我们还简要概述了在 LMIC 中成功实施和部署卫生系统中技术的几个关键组成部分，包括关于机器学习解决方案的开发过程中的技术和文化考量。然后，我们利用这些经验，解决在资源有限的环境中影响较大的问题，以及 指明AI/ML 可以提供最大助益的领域。
原文标题：Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care
原文：The health needs of those living in resource-limited settings are a vastly overlooked and understudied area in the intersection of machine learning (ML) and health care. While the use of ML in health care is more recently popularized over the last few years from the advancement of deep learning, low-and-middle income countries (LMICs) have already been undergoing a digital transformation of their own in health care over the last decade, leapfrogging milestones due to the adoption of mobile health (mHealth). With the introduction of new technologies, it is common to start afresh with a top-down approach, and implement these technologies in isolation, leading to lack of use and a waste of resources. In this paper, we outline the necessary considerations both from the perspective of current gaps in research, as well as from the lived experiences of health care professionals in resource-limited settings. We also outline briefly several key components of successful implementation and deployment of technologies within health systems in LMICs, including technical and cultural considerations in the development process relevant to the building of machine learning solutions. We then draw on these experiences to address where key opportunities for impact exist in resource-limited settings, and where AI/ML can provide the most benefit.
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