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Keeping an eye on eye care: monitoring progress towards effective coverage.关注眼保健:监测有效覆盖方面的进展。
Lancet Glob Health. 2021 Oct;9(10):e1460-e1464. doi: 10.1016/S2214-109X(21)00212-6. Epub 2021 Jul 5.
2
The changing perspective of clinical trial designs.临床试验设计不断变化的视角。
Perspect Clin Res. 2021 Apr-Jun;12(2):66-71. doi: 10.4103/picr.PICR_138_20. Epub 2021 Jan 8.
3
Global economic productivity losses from vision impairment and blindness.视力损害和失明造成的全球经济生产力损失。
EClinicalMedicine. 2021 Apr 26;35:100852. doi: 10.1016/j.eclinm.2021.100852. eCollection 2021 May.
4
Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies.数字健康在公共卫生应对 COVID-19 中的应用:对人工智能、远程医疗及相关技术的系统综述
NPJ Digit Med. 2021 Feb 26;4(1):40. doi: 10.1038/s41746-021-00412-9.
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The Lancet Global Health Commission on Global Eye Health: vision beyond 2020.《柳叶刀》全球眼健康委员会:2020年之后的愿景。
Lancet Glob Health. 2021 Apr;9(4):e489-e551. doi: 10.1016/S2214-109X(20)30488-5. Epub 2021 Feb 16.
6
Digital health during COVID-19: lessons from operationalising new models of care in ophthalmology.新冠疫情期间的数字健康:在眼科实施新型护理模式方面的经验教训。
Lancet Digit Health. 2021 Feb;3(2):e124-e134. doi: 10.1016/S2589-7500(20)30287-9.
7
Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the Global Burden of Disease Study.30 多年来盲症和远距离及近距离视力损伤流行率的变化趋势:全球疾病负担研究的分析。
Lancet Glob Health. 2021 Feb;9(2):e130-e143. doi: 10.1016/S2214-109X(20)30425-3. Epub 2020 Dec 1.
8
mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study.使用机器学习提高糖尿病和抑郁症患者身体活动水平的移动医疗应用:DIAMANTE 研究的临床试验方案。
BMJ Open. 2020 Aug 20;10(8):e034723. doi: 10.1136/bmjopen-2019-034723.
9
Implementation Research: An Efficient and Effective Tool to Accelerate Universal Health Coverage.实施研究:加快全民健康覆盖的高效有力工具
Int J Health Policy Manag. 2020 May 1;9(5):182-184. doi: 10.15171/ijhpm.2019.125.
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人工智能在实现可持续发展目标方面的贡献:从商业行业和机器学习在眼健康计划中的应用早期经验中学到什么。

The Contribution of Artificial Intelligence in Achieving the Sustainable Development Goals (SDGs): What Can Eye Health Can Learn From Commercial Industry and Early Lessons From the Application of Machine Learning in Eye Health Programmes.

机构信息

The International Centre for Eye Health (ICEH), London School of Hygiene and Tropical Medicine, London, United Kingdom.

Peek Vision, London, United Kingdom.

出版信息

Front Public Health. 2021 Dec 22;9:752049. doi: 10.3389/fpubh.2021.752049. eCollection 2021.

DOI:10.3389/fpubh.2021.752049
PMID:35004574
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8727468/
Abstract

Achieving The United Nations sustainable developments goals by 2030 will be a challenge. Researchers around the world are working toward this aim across the breadth of healthcare. Technology, and more especially artificial intelligence, has the ability to propel us forwards and support these goals but requires careful application. Artificial intelligence shows promise within healthcare and there has been fast development in ophthalmology, cardiology, diabetes, and oncology. Healthcare is starting to learn from commercial industry leaders who utilize fast and continuous testing algorithms to gain efficiency and find the optimum solutions. This article provides examples of how commercial industry is benefitting from utilizing AI and improving service delivery. The article then provides a specific example in eye health on how machine learning algorithms can be purposed to drive service delivery in a resource-limited setting by utilizing the novel study designs in response adaptive randomization. We then aim to provide six key considerations for researchers who wish to begin working with AI technology which include collaboration, adopting a fast-fail culture and developing a capacity in ethics and data science.

摘要

到 2030 年实现联合国可持续发展目标将是一项挑战。世界各地的研究人员正在医疗保健、技术等各个领域朝着这一目标努力。人工智能具有推动我们前进和支持这些目标的能力,但需要谨慎应用。人工智能在医疗保健领域显示出巨大的潜力,在眼科、心脏病学、糖尿病和肿瘤学等领域取得了快速发展。医疗保健行业开始向商业行业领导者学习,这些领导者利用快速和持续的测试算法来提高效率并找到最佳解决方案。本文提供了一些例子,说明商业行业如何从利用人工智能中受益,并提高服务交付水平。然后,本文在眼健康方面提供了一个具体的例子,说明如何通过利用响应适应性随机化的新型研究设计,利用机器学习算法来推动资源有限环境下的服务交付。然后,我们旨在为希望开始使用人工智能技术的研究人员提供六个关键考虑因素,包括合作、采用快速失败文化以及在伦理和数据科学方面培养能力。