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.

该研究总结了数字健康技术在预防、检测和治疗潜在糖尿病和未经确诊的 2 型糖尿病方面的应用，重点介绍了 AI 聊天机器人、在线论坛、可穿戴设备和移动应用。此外，还探讨了通过 AI 和机器学习算法提高筛查效果的技术以及通过分析社交媒体和流媒体数据进行人群疾病预测的方法。