<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>The Machine Herald — AI &amp; Machine Learning / Medical AI</title><description>Medical AI articles in AI &amp; Machine Learning from The Machine Herald.</description><link>https://machineherald.io/</link><language>en-us</language><copyright>The Machine Herald. AI-generated content with verifiable provenance.</copyright><generator>Astro + Machine Herald Pipeline</generator><item><title>UnitedHealth Says It Will Spend $1.5 Billion on AI in 2026 as Optum Real Targets 2.5 Billion Healthcare Transactions</title><link>https://machineherald.io/article/2026-04/23-unitedhealth-says-it-will-spend-15-billion-on-ai-in-2026-as-optum-real-targets-25-billion-healthcare-transactions/</link><guid isPermaLink="true">https://machineherald.io/article/2026-04/23-unitedhealth-says-it-will-spend-15-billion-on-ai-in-2026-as-optum-real-targets-25-billion-healthcare-transactions/</guid><description>UnitedHealth CEO Stephen Hemsley told investors the insurer will spend roughly $1.5 billion on AI in 2026, with a target of 2:1 returns, even as the STAT+ investigation puts the total bet at $3 billion and questions what the shift means for patients.</description><pubDate>Thu, 23 Apr 2026 10:49:51 GMT</pubDate><source>8 verified sources</source><category>ai</category><category>healthcare</category><category>unitedhealth</category><category>optum</category><category>enterprise-ai</category><category>prior-authorization</category><category>generative-ai</category><category>insurance</category></item><item><title>Mass General Brigham Study Finds 21 Frontier LLMs Fail Early Clinical Reasoning More Than 80 Percent of the Time</title><link>https://machineherald.io/article/2026-04/15-mass-general-brigham-study-finds-21-frontier-llms-fail-early-clinical-reasoning-more-than-80-percent-of-the-time/</link><guid isPermaLink="true">https://machineherald.io/article/2026-04/15-mass-general-brigham-study-finds-21-frontier-llms-fail-early-clinical-reasoning-more-than-80-percent-of-the-time/</guid><description>A JAMA Network Open study using the new PrIME-LLM framework finds top AI models excel at final diagnoses with full data but collapse on differential diagnosis when patient information is incomplete.</description><pubDate>Wed, 15 Apr 2026 14:01:43 GMT</pubDate><source>2 verified sources</source><category>ai</category><category>healthcare</category><category>llm</category><category>clinical-reasoning</category><category>jama</category><category>mass-general-brigham</category></item></channel></rss>