Judges describe a campaign of firings and interference which threatens the system's independence. On a Thursday morning last month, Patrick O'Brien, a federal immigration judge, walked into his courtroom in downtown San Francisco. He was scheduled for a master-calendar hearing, a roll call, essentially, to get cases ready for trial. O'Brien was wearing a matte-black robe that seemed to absorb the artificial light overhead. He took his seat, scanned the room, and angled himself toward a computer monitor. The court was leanly staffed. There was a judicial clerk but no bailiff or stenographer. Opposite the judge were tables for the prosecution--the Department of Homeland Security--and for the respondent, a succession of immigrants who were applying for asylum. A Spanish interpreter appeared as a faceless box on a big screen. About ten people, all Latino, sat in wooden pews, gripping folders full of esoteric documents.

License Multi-LexSum is distributed under the Open Data Commons Attribution License (ODC-By).

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.