This document provides responses to the FDA's request for public comments (Docket No FDA 2023 N 4853) on the role of digital health technologies (DHTs) in detecting prediabetes and undiagnosed type 2 diabetes. It explores current DHT applications in prevention, detection, treatment and reversal of prediabetes, highlighting AI chatbots, online forums, wearables and mobile apps. The methods employed by DHTs to capture health signals like glucose, diet, symptoms and community insights are outlined. Key subpopulations that could benefit most from remote screening tools include rural residents, minority groups, high-risk individuals and those with limited healthcare access. Capturable high-impact risk factors encompass glycemic variability, cardiovascular parameters, respiratory health, blood biomarkers and patient reported symptoms. An array of non-invasive monitoring tools are discussed, although further research into their accuracy for diverse groups is warranted. Extensive health datasets providing immense opportunities for AI and ML based risk modeling are presented. Promising techniques leveraging EHRs, imaging, wearables and surveys to enhance screening through AI and ML algorithms are showcased. Analysis of social media and streaming data further allows disease prediction across populations. Ongoing innovation focused on inclusivity and accessibility is highlighted as pivotal in unlocking DHTs potential for transforming prediabetes and diabetes prevention and care.

翻译：本文档回应了美国食品药品监督管理局（FDA）关于数字健康技术（DHT）在检测糖尿病前期和未确诊2型糖尿病中作用的公众意见征集（案卷编号 FDA 2023 N 4853）。研究探讨了当前DHT在糖尿病前期预防、检测、治疗和逆转中的应用，重点关注AI聊天机器人、在线论坛、可穿戴设备和移动应用程序。概述了DHT捕获健康信号（如血糖、饮食、症状和社区见解）所采用的方法。能从远程筛查工具中最大获益的关键亚群包括农村居民、少数群体、高风险个体以及医疗资源有限的人群。可捕获的高影响风险因素涵盖血糖变异性、心血管参数、呼吸健康、血液生物标志物和患者报告的症状。讨论了一系列无创监测工具，但需进一步研究其在多样化人群中的准确性。本文展示了提供基于AI和ML风险建模巨大机遇的广泛健康数据集，并重点介绍了利用电子健康记录（EHR）、影像学、可穿戴设备和调查问卷，通过AI和ML算法增强筛查的有效技术。对社交媒体和流数据的分析进一步实现了跨人群疾病预测。持续聚焦包容性和可及性的创新，被认为是释放DHT在改变糖尿病前期及糖尿病预防与护理中潜力的关键。