Williams Douglas, Hornung Heiko, Nadimpalli Adi, Peery Ashton
Harvard, MA, United States.
D-tree International, Zanzibar, Tanzania.
Front Artif Intell. 2021 Apr 29;4:553987. doi: 10.3389/frai.2021.553987. eCollection 2021.
As anyone who has witnessed firsthand knows, healthcare delivery in low-resource settings is fundamentally different from more affluent settings. Artificial Intelligence, including Machine Learning and more specifically Deep Learning, has made amazing advances over the past decade. Significant resources are now dedicated to problems in the field of medicine, but with the potential to further the digital divide by neglecting underserved areas and their specific context. In the general case, Deep Learning remains a complex technology requiring deep technical expertise. This paper explores advances within the narrower field of deep learning image analysis that reduces barriers to adoption and allows individuals with less specialized software skills to effectively employ these techniques. This enables a next wave of innovation, driven largely by problem domain expertise and the creative application of this technology to unaddressed concerns in LMIC settings. The paper also explores the central role of NGOs in problem identification, data acquisition and curation, and integration of new technologies into healthcare systems.
任何有过亲身经历的人都知道，资源匮乏地区的医疗服务与资源较为丰富的地区有着根本的不同。包括机器学习，更具体地说是深度学习在内的人工智能在过去十年中取得了惊人的进展。现在有大量资源投入到医学领域的问题上，但也有可能因忽视服务不足的地区及其特定背景而加剧数字鸿沟。一般来说，深度学习仍然是一项复杂的技术，需要深厚的技术专长。本文探讨了深度学习图像分析这一较窄领域内的进展，这些进展降低了采用障碍，并使软件技能不太专业的个人能够有效运用这些技术。这推动了下一波创新浪潮，这一浪潮主要由问题领域专业知识以及将该技术创造性地应用于低收入和中等收入国家（LMIC）环境中未得到解决的问题所驱动。本文还探讨了非政府组织在问题识别、数据获取与管理以及将新技术整合到医疗系统中的核心作用。
