<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AI in Healthcare on Aneesh Sathe</title><link>https://aneeshsathe.com/tags/ai-in-healthcare/</link><description>Recent content in AI in Healthcare on Aneesh Sathe</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 23 Jul 2025 05:50:31 +0000</lastBuildDate><atom:link href="https://aneeshsathe.com/tags/ai-in-healthcare/index.xml" rel="self" type="application/rss+xml"/><item><title>AI: Explainable Enough</title><link>https://aneeshsathe.com/ai-explainable-enough/</link><pubDate>Wed, 23 Jul 2025 05:50:31 +0000</pubDate><guid>https://aneeshsathe.com/ai-explainable-enough/</guid><description>&lt;p&gt;They look really juicy, she said. I was sitting in a small room with a faint chemical smell, doing one my first customer interviews. There is a sweet spot between going too deep and asserting a position. Good AI has to be just explainable enough to satisfy the user without overwhelming them with information. Luckily, I wasn’t new to the problem.&lt;/p&gt;


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&lt;p&gt;Coming from a microscopy and bio background with a strong inclination towards image analysis I had picked up deep learning as a way to be lazy in lab. Why bother figuring out features of interest when you can have a computer do it for you, was my angle. The issue was that in 2015 no biologist would accept any kind of deep learning analysis and definitely not if you couldn’t explain the details.&lt;/p&gt;</description></item><item><title>Domain Ontologies: Indispensable for Knowledge Graph Construction</title><link>https://aneeshsathe.com/domain-ontologies-indispensable-for-knowledge-graph-construction/</link><pubDate>Wed, 15 Jan 2025 08:06:00 +0000</pubDate><guid>https://aneeshsathe.com/domain-ontologies-indispensable-for-knowledge-graph-construction/</guid><description>&lt;p&gt;AI slop is all around and increasingly extraction of useful information will face difficulties as we start to feed more noise into the already noisy world of knowledge. We are in an era of unprecedented data abundance, yet this deluge of information often lacks the structure necessary to derive meaningful insights. &lt;strong&gt;Knowledge graphs (KGs), with their ability to represent entities and their relationships as interconnected nodes and edges, have emerged as a powerful tool for managing and leveraging complex data&lt;/strong&gt;. However, the efficacy of a KG is critically dependent on the underlying structure provided by domain ontologies. These ontologies, which are formal, machine-readable conceptualizations of a specific field of knowledge, are not merely useful, but essential for the creation of robust and insightful KGs. Let&amp;rsquo;s explore the role that domain ontologies play in scaffolding KG construction, drawing on various fields such as AI, healthcare, and cultural heritage, to illuminate their importance.&lt;/p&gt;</description></item></channel></rss>