<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Selected Publications on Jonathan Huang</title><link>https://jonhuang.net/papers/</link><description>Recent content in Selected Publications on Jonathan Huang</description><image><title>Jonathan Huang</title><url>https://jonhuang.net/picture2.jpg</url><link>https://jonhuang.net/picture2.jpg</link></image><generator>Hugo -- 0.147.3</generator><language>en</language><lastBuildDate>Thu, 05 Jun 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://jonhuang.net/papers/index.xml" rel="self" type="application/rss+xml"/><item><title>Efficiency and Quality of Generative AI–Assisted Radiograph Reporting</title><link>https://jonhuang.net/papers/jno2025/</link><pubDate>Thu, 05 Jun 2025 00:00:00 +0000</pubDate><guid>https://jonhuang.net/papers/jno2025/</guid><description>We investigate how clinical implementation of a generative AI model for all-radiograph report generation improves reporting efficiency.</description></item><item><title>Prediction and Detection of Glaucomatous Visual Field Progression Using Deep Learning on Macular Optical Coherence Tomography</title><link>https://jonhuang.net/papers/glauc/</link><pubDate>Fri, 05 Apr 2024 00:00:00 +0000</pubDate><guid>https://jonhuang.net/papers/glauc/</guid><description>We investigate how unsupervised learning methods applied to macular optical coherence tomography may detect and predict glaucoma progression.</description></item><item><title>Generative Artificial Intelligence for Chest Radiograph Interpretation in the Emergency Department</title><link>https://jonhuang.net/papers/jno2023/</link><pubDate>Thu, 05 Oct 2023 00:00:00 +0000</pubDate><guid>https://jonhuang.net/papers/jno2023/</guid><description>We describe a clinical evaluation of a generative AI model for chest radiograph report generation in the emergency department setting.</description></item></channel></rss>