Artwork
iconShare
 
Manage episode 498239803 series 1237354
Content provided by MIT CSAIL Alliances and CSAIL Alliances. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by MIT CSAIL Alliances and CSAIL Alliances or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.
AI is transforming radiology, but not at the expense of skilled technicians. In the same way that personal computers and spreadsheets didn’t eliminate accountants, AI is not going to replace radiologists but will instead transform the way they work. MIT CSAIL Professor Polina Golland’s research sits at the intersection of machine learning and healthcare, specifically medical imaging. In this episode, she discusses her team’s groundbreaking work on algorithms that analyze subtle patterns in x-rays, helping detect diseases earlier and understand them more deeply. This conversation covers: 2:00 - How do doctors diagnose heart failure? 5:27 - Converting medical imagery to numbers 8:20 - Code generation for radiologists 9:25 - Weaknesses in the medical system that computing can strengthen 16:48 - The choreography of treating a patient 20:31 - Turning an algorithm into a product 24:26 - Will radiologists be replaced by AI? 30:21 - How will AI change medical imagery? Connect with CSAIL Alliances: On our site: cap.csail.mit.edu/about-us/meet-our-team On LinkedIn: linkedin.com/company/mit-csail #MITCSAIL #AI #GenerativeAI #Leadership #Technology #CSAILPodcast
  continue reading

70 episodes