<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="https://media.rss.com/style.xsl"?>
<rss xmlns:podcast="https://podcastindex.org/namespace/1.0" xmlns:itunes="http://www.itunes.com/dtds/podcast-1.0.dtd" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" xml:lang="en" version="2.0">
  <channel>
    <title><![CDATA[Explainable AI for Health]]></title>
    <link>https://rss.com/podcasts/xaihealth</link>
    <atom:link href="https://media.rss.com/xaihealth/feed.xml" rel="self" type="application/rss+xml"/>
    <atom:link rel="hub" href="https://hub.livewire.io/"/>
    <description><![CDATA[Join us as we delve into the world of user-centered explainable AI through conversations with leading researchers, designers, and entrepreneurs. We explore how explainability techniques bridge the “last-mile” between model predictions and actionable insights for patients, caregivers, and healthcare providers to use AI in a trustworthy, fair, and workflow-compatible manner.

The University of Texas at Austin's AI Health Lab is led by Prof. Ying Ding, PhD from the School of information and Prof. Justin Rousseau, MD, MMSc,  from the UT Austin Dell Medical School. We focus on cutting-edge research on AI in health and data-driven science of science. On this channel, we interview the foremost experts on Explainable AI in Health. 

Hosted by PhD candidate Madalyn Rosenthal and honors neuroscience undergraduate Ian Alrahwan. Produced by Ian Alrahwan. ]]></description>
    <generator>RSS.com v2.5.4</generator>
    <lastBuildDate>Thu, 16 Feb 2023 19:14:58 GMT</lastBuildDate>
    <language>en</language>
    <copyright><![CDATA[Ian Alrahwan Ying Ding 2022]]></copyright>
    <itunes:image href="https://media.rss.com/xaihealth/20220609_070614_afb7f79942eeb12ba4dd3cac6e3a82cf.jpg"/>
    <image>
      <url>https://media.rss.com/xaihealth/20220609_070614_afb7f79942eeb12ba4dd3cac6e3a82cf.jpg</url>
      <title>Explainable AI for Health</title>
      <link>https://rss.com/podcasts/xaihealth</link>
    </image>
    <podcast:locked>yes</podcast:locked>
    <itunes:author>The University of Texas at Austin AI Heath Lab</itunes:author>
    <itunes:owner>
      <itunes:name>The University of Texas at Austin AI Heath Lab</itunes:name>
    </itunes:owner>
    <itunes:explicit>no</itunes:explicit>
    <itunes:type>episodic</itunes:type>
    <itunes:category text="Business">
      <itunes:category text="Entrepreneurship"/>
    </itunes:category>
    <itunes:category text="Health &amp; Fitness">
      <itunes:category text="Medicine"/>
    </itunes:category>
    <item>
      <title><![CDATA[Interview with IBM's Dr. Prithwish Chakraborty]]></title>
      <itunes:title><![CDATA[Interview with IBM's Dr. Prithwish Chakraborty]]></itunes:title>
      <description><![CDATA[<p>In this episode we interview Dr. Prithwish Chakraborty! Dr. Chakraborty is currently a Research Staff Member and global sub-theme lead at IBM Research in the Center of Computational Health at the IBM T.J. Watson Research Center, NY. His work focuses on applications of data science towards patient health characterization and risk modeling. Broadly, his research interests are temporal data mining, machine learning and causal inference. Please checkout his work at: <a href="https://prithwi.github.io/">https://prithwi.github.io/</a></p>]]></description>
      <link>https://rss.com/podcasts/xaihealth/515092</link>
      <enclosure url="https://media.rss.com/xaihealth/20220609_080639_f1f2b5d836fff2a773dbb24dac6beca7.mp3" length="45589204" type="audio/mpeg"/>
      <guid isPermaLink="false">2d3ff9d9-6af8-4d3a-accd-ae79bd68c231</guid>
      <itunes:image href="https://media.rss.com/xaihealth/20220612_020650_b633fc3a60dd1adf00a1588073a9730b.jpg"/>
      <itunes:duration>1413</itunes:duration>
      <itunes:episodeType>full</itunes:episodeType>
      <itunes:episode>1</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:explicit>no</itunes:explicit>
      <pubDate>Thu, 09 Jun 2022 20:13:15 GMT</pubDate>
    </item>
  </channel>
</rss>