The MaML Podcast - Medicine & Machine Learning Twitter - @themamlpodcast | TikTok - @maml_podcast
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The MaML Podcast is brought to you by medical residents, grad students, and med students passionate about the new frontier of healthcare and AI. We feature interviews with prominent figures in industry, academia, and medicine. This podcast is designed for anyone with a budding interest in the field.
Created by David JH Wu, Aaron Schumacher, and Saurin Kantesaria.
Hosts: David JH Wu, Maddie Ahern, Raeesa Kabir
Producers: Aaron Schumacher, Kirsi Oldenburg, Melanie Bussan
Talent: Alex Jacobs, Heather Nelson
Media: Nikhil Kapur
Message us! contact@themamlpodcast.com Support this podcast: https://podcasters.spotify.com/pod/show/maml-podcast/support
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Dr. Nina Kottler - AI & Radiology: Then, Now, and Beyond
Dr. Nina Kottler is the associate chief medical officer of clinical artificial intelligence and vice president of clinical operations for Radiology Partners, the largest radiology practice in the US, serving over 3,250 hospitals and other healthcare facilities, interpreting over 53 million exams annually.
Host: David Wu
Twitter: @davidjhwu
Audio Producer: Aaron Schumacher
LinkedIn: Aaron Schumacher
Video Editor + Art: Saurin Kantesaria
Instagram: saorange314
00:00:58 What brought you to the intersection of medicine and artificial intelligence?
00:07:00 The importance of translating between clinicians and AI engineers
00:12:54 The origins of Radiology Partners
00:16:40 Dr. Kottler’s start in Teleradiology
00:21:18 The transition form analog to digital in Radiology
00:27:35 The current state of Radiology Partners
00:32:00 When did Dr. Kottler become a leader in the AI projects?
00:45:00 AI models that Radiology Partners use
00:52:00 Fragility, Technological Evaluation and Business evaluation in Radiology AI systems
00:56:10 Dr. Kottler’s thoughts on what the future of AI and Radiology will look like.
01:00:30 Dr. Kottler’s advice for people in medicine desiring unique paths.
01:02:45 What brings you joy?
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Munjal Shah - Hippocratic AI: A Safety-First Healthcare LLM
Munjal Shah is the co-founder and CEO of Hippocratic AI, a new startup in Generative AI + Healthcare. Hippocratic is building a safety-focused large language model specifically built for the healthcare industry.
Host: David Wu
Twitter: @davidjhwu
Audio Producer: Aaron Schumacher
LinkedIn: Aaron Schumacher
Video Editor + Art: Saurin Kantesaria
Instagram: saorange314
Time Stamps:
00:00:58 What brought you to the intersection of medicine and artificial intelligence?
00:06:20 Overview of the American Healthcare System
00:08:06 Hippocratic AI and the Adherence Problem within healthcare
00:14:30 Building an AI Chronic Care Nurse for specific conditions
00:17:15 AI systems and medical co-morbidities
00:24:00 The process of building Hippocratic AI
00:32:45 Becoming more efficient than ChatGPT4
00:33:48 Navigating the problem of hallucinations with Hippocratic AI
00:39:30 How close are we to Health General Intelligence (HGI)?
00:45:40 What advice would you give to someone interested in starting their own company?
00:48:20 How did mentorship shape your path?
00:49:40 What brings you joy?
00:52:25 How do you find novel ideas for start-ups?
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Dr. Muhammad Mamdani - AI Research in Healthcare Policy and Education
Dr. Mamdani is a professor, pharmacist, and epidemiologist. He is the Vice President of Data Science and Advanced Analytics at Unity Health Toronto and Director of the University of Toronto Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM). Dr. Mamdani’s team bridges advanced analytics including machine learning with clinical and management decision making to improve patient outcomes and hospital efficiency. Dr. Mamdani is also Professor in the Temerty Faculty of Medicine, the Leslie Dan Faculty of Pharmacy, and the Institute of Health Policy, Management and Evaluation of the Dalla Lana School of Public Health at the University of Toronto. He is also a Faculty Affiliate of the Vector Institute. He has published over 500 studies in peer-reviewed journals.
Host: Raeesa Kabir
Audio Producer: Melanie Bussan
Video Editor + Art: Saurin Kantesaria
Instagram: saorange314
Social Media: Nikhil Kapur
Time Stamps:
0:00 Dr. Mamdani’s Background and Career Path
9:30 Where current data driven medicine strategies fall short and how AI can step in
17:00 How Dr. Mamdani’s work in AI and machine learning began
22:00 Applied Health Research Center and the Ontario Policy Research Network
28:45 The impact of utilizing machine learning and AI at the level of patient care - Chart Watch
35:50 Logistics of Developing and Implementing AI solutions
39:10 Insights Gained - From Purpose to Implementation
43:30 Directing Multiple Projects - Recruitment of AI Team
47:45 Future Projects: Back to AI Basics
54:15 Future of AI in Medicine - Fostering trust in AI
57:20 Advice to Younger Self
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Spezi (Stanford CardinalKit) - An Open Source Framework for Digital Health
CardinalKit (now Spezi) is an open-source framework for Digital Health Applications and Research. They were recently featured in the news for releasing HealthGPT, an experimental iOS app that lets you query your health data. Spezi is housed in the Stanford Byers Center for Biodesign and directed by Oliver Aalami, MD with Vishnu Ravi, MD as lead architect. Also joining us on this interview is postdoc Paul Schmiedmayer, PhD.
Spezi provides a suite of tools to build modern, interoperable digital health tools from the ground up, from the app itself to storing and analyzing collected data in the cloud. It is designed to accelerate rapid prototyping of digital health applications by reducing costs by as much as 75% (~$150,000) and timelines by 12 months.
Host: David Wu
Twitter: @davidjhwu
Audio Producer + Video Editor + Art: Saurin Kantesaria
Instagram: saorange314
Social Media: Nikhil Kapur
Time Stamps:
00:58 - The expertise behind Spezi (CardinalKit)
08:03 - Healthcare has a lack of data standardization + Why you should know about HL7 FHIR
14:13 - How did Spezi (CardinalKit) become what it is today?
18:26 - Drink Spezi!
19:53 - Making code/healthcare data more modular and user-friendly
26:40 - Translating a med student's sensor research to a useable device for kids with cerebral palsy
31:20 - From a $40,000 eczema patch test in clinic to a completely at-home test
35:45 - Using healthGPT to make health data easy to understand for patients (LLM on FHIR)
42:35 - How do you deal with privacy issues?
49:33 - What do you think the future of AI in medicine will look like in 10-20 years?
52:00 - Applications where using only an LLM doesn't always work (a case for hybrid systems)
55:30 - What brings you joy?
58:43 - What makes a successful digital health team?
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Dereck Paul - GlassHealth: AI-Assisted Diagnosis and Clinical Decision-Making
Dereck Paul, MD is a cofounder and the CEO of Glass Health, an AI-powered medical knowledge management and clinical decision-making platform that helps clinicians provide better patient care. Previously, he was an internal medicine resident at Brigham and Women's Hospital, Harvard Medical School and a medical student at the UCSF School of Medicine.
Host: David Wu
Twitter: @davidjhwu
Audio Producer + Video Editor + Art: Saurin Kantesaria
Instagram: saorange314
Social Media: Nikhil Kapur
Time Stamps:
01:13 - From music major to med school to making a startup
06:30 - Poor healthcare technology = physician burnout, the motivation for building Glass Health
09:15 - Glass Notebook - "Notion for doctors"
11:24 - Building a startup in the era of Chat-GPT
13:50 - What doctors need in an AI-assisted diagnosis software
19:15 - Transition towards a more AI oriented technology - Glass AI
23:00 - How does Glass AI make accurate diagnoses?
28:40 - Why doctors need to be involved in building clinical AI products
30:50 - Practical usage of Glass AI in the clinic
33:04 - Why Glass AI will be more trustworthy than Chat-GPT in writing clinical notes
37:43 - Why LLMs don't need to be perfect for use in the clinic
40:28 - Ethical implications of Glass AI and similar products
45:34 - Should we disclose when we use AI to write a clinical note?
49:13 - What do you think the future of AI in medicine will look like in 10-20 years?
52:30 - What brings you joy? What gives your life meaning?
56:10 - Would you ever go back to being a musician?
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Jerry Liu - Building LlamaIndex, the Data Framework for LLMs
Jerry Liu is the co-founder and creator of LlamaIndex (formerly known as GPT-Index), an interface that allows users to connect their data to LLM’s such as Chat-GPT. He has a B.S. in Computer Science from Princeton and has worked at companies such as Quora, Uber, and Robust Intelligence prior to starting LlamaIndex.
Host: David Wu
Twitter: @davidjhwu
Audio Producer: Aaron Schumacher
LinkedIn: Aaron Schumacher
Video Editor + Art: Saurin Kantesaria
Instagram: saorange314
Social Media: Nikhil Kapur
Time Stamps:
01:25 The path to starting LlamaIndex + initial ideas
07:09 LLMs like Chat-GPT vs traditional machine learning
10:00 4 steps of traditional machine learning
10:45 How do large LLMs change the game?
14:11 How does LlamaIndex help LLMs work with unstructured data?
18:08 How do you work with gigabytes of private data?
19:57 Organizing words and paragraphs by topic with embeddings
24:55 The importance of structuring data
26:00 3 key abstractions in LlamaIndex
29:25 Medical use cases for LlamaIndex
31:29 Increasing efficiency in medicine
33:25 An AI medical Research Assistant (Insight)
34:31 Other methods of connecting LLMs to data
36:55 What is langchain?
39:56 What work in the AI and LLM space excites you the most?
42:23 Do you ever feel scared about the developments of AI?
43:45 Llamas and Machine Learning
45:36 What do you think the future of AI in medicine will look like in 10-20 years?
47:24 What advice would you give to grad students, med students, and other early career professionals getting into AI and medicine?
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Support this podcast: https://podcasters.spotify.com/pod/show/maml-podcast/support
Customer Reviews
Brilliant
Very interesting topics by the brilliant author. A must for anybody interested in medicine. A great way to stay on top of the developments in the world of machine learning. Very well thought out and delivered Podcasts.
Great Podcast for Curious People
Super interesting guests from diverse backgrounds! Great podcast for healthcare providers and medical students but also just for anyone interested in this topic.
I’m here for it
Great conversations. As someone not in the field, I appreciate the casual interview style.