<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Clinical AI on Abhinav Dadhich</title><link>http://resbyte.github.io/tags/clinical-ai/</link><description>Recent content in Clinical AI on Abhinav Dadhich</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 23 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="http://resbyte.github.io/tags/clinical-ai/index.xml" rel="self" type="application/rss+xml"/><item><title>Foundation Models for ECG: Promise, Gaps, and What Comes Next</title><link>http://resbyte.github.io/foundation-models-for-ecg-promise-gaps-and-what-comes-next/</link><pubDate>Sat, 23 May 2026 00:00:00 +0000</pubDate><guid>http://resbyte.github.io/foundation-models-for-ecg-promise-gaps-and-what-comes-next/</guid><description>&lt;p&gt;Large pretrained models have reshaped NLP and computer vision. The natural question is whether the same paradigm — pretrain on huge unlabeled corpora, fine-tune on small labeled datasets — translates to clinical time-series. For ECG specifically, the stakes are high: cardiac events kill more people than any other cause, and most of the world has no cardiologist nearby.&lt;/p&gt;
&lt;p&gt;The short answer is: partially. And the gaps are instructive.&lt;/p&gt;
&lt;h2 id="why-ecg-is-a-good-testbed"&gt;Why ECG is a good testbed&lt;/h2&gt;
&lt;p&gt;ECG is well-suited for this experiment for a few reasons:&lt;/p&gt;</description></item></channel></rss>