The MaML Podcast - Medicine & Machine Learning

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?

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?

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

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?

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?

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?

Dr. Ryan earned both a doctorate of medicine (M.D.) and master in public health (M.P.H.) degree from the University of Connecticut in 2001. He completed his postdoctoral training at Harvard's Beth Israel Deaconess Medical Center in Boston, including a chief residency and cardiology fellowship. In 2014 Dr. Ryan started Boards and Beyond, an online lecture library used by medical students across the world to prepare for board exams. In 2022, Dr. Ryan sold his company to McGraw Hill and will continue working to build medical education materials. 

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:

00:55 - How did you come to create Boards and Beyond 

08:00 - What was it like to make videos outside of your specialty

09:30 - The launch of Boards and Beyond 

12:22 - Designing the Curriculum for Boards and Beyond 

15:10 - Jason Ryan on selling Boards and Beyond to McGraw Hill 

16:58 - What is next for Jason Ryan?

18:00 - Who were Jason Ryan’s favorite teachers 

19:48 - What makes a good teacher 

23:40 - What are your thoughts on the future of artificial intelligence and medical education

30:03 - Thoughts on Khan Academy’s AI-based Khanmigo 

31:25 - Jason Ryan’s thoughts on becoming a clinician 

35:29 - Mentorship throughout Jason Ryan’s career 

37:35 - Could medical training be shortened?

41:40 - What do you think the future of medicine and artificial intelligence will look like?

43:10 - What advice would you give medical students today?

46:14 - What brings you joy and meaning? What are your greatest fears?

52:48 - What was your lowest point in medical training and how did you overcome it?

Mushtaq Bilal is a postdoctoral researcher at the University of Southern Denmark. He earned his PhD in comparative literature from Binghamton University. He works on simplifying the process of academic writing and writes about ethical use of artificial intelligence for academic purposes.

Host: Raeesa Kabir

Audio Producer: Melanie Bussan

Video Editor + Art: Saurin Kantesaria

Instagram: saorange314

Social Media: Nikhil Kapur

Music: Caligula - Windows96. Used with Artist's Permission.

Introduction and Mushtaq’s path: 0:00 seconds 

Overview on using AI tools for efficient writing: 8:00 seconds

Keeping up to date with all the new apps: 18:00 seconds 

Leveling the playing field of academia: 23:15 seconds 

Ethical considerations of AI powered writing tool: 40:30 seconds

Mushtaq’s tutorial for simplifying the academic writing process: 53:20 seconds

Fun ending question and ending: 57:30

John Kang, MD, Ph.D. is an assistant professor of Radiation Oncology and Biomedical Informatics Lead at the University of Washington in Seattle. His research interests include the application of Natural Language Processing (NLP) to examine trends in the MaML space. He is a physician-data scientist passionate about uncovering the complex interactions underneath large datasets. He has over 10 years of experience in the novel applications of computational modeling and machine learning in biology systems. 

Host: David Wu

Twitter: @davidjhwu

Audio Producer: Aaron Schumacher

Twitter: a_schu95

Video Editor + Art: Saurin Kantesaria

Instagram: saorange314

Social Media: Nikhil Kapur

00:45 Could you tell us about your journey to the intersection of medicine and machine learning

07:40 Balancing Residency Training and staying caught up on research in the machine learning space

16:00 Using machine learning to understand biostatistics 

18:12 How would you describe the research that you find the most exciting / Unsupervised learning 

23:00 Overview of Word Embedding and addressing  potential bias 

29:25 Dr. Kang’s application of word embedding for research funding 

42:52 The intersection of artificial intelligence and human intelligence 

45:35 T-SNE / T-Distributed Stochastic Neighbor Embedding in grant analysis 

50:50 Has T-SNE helped guide Dr. Kang’s research and grant writing 

57:00 The future of creativity and ChatGPT 

01:02:30 Fear vs Hope in the Medicine and Machine Learning space 

01:07:00 What do you think is the future of the MaML space in the next 10-20 years?

01:11:02 What advice would you give yourself as you were finishing medical school?

Dr. Kaz Nelson is a Fellow of the American Board of Psychiatry and Neurology and serves as Associate Professor in the Department of Psychiatry and Behavioral Sciences and the Associate Designated Institutional Official in the Office of Graduate Medical Education at the University of Minnesota Medical School. She is also host of “The Mind Deconstructed.” In this podcast, Nelson and her brother George dispel myths, address listener questions, and inform the public about mental health and a life worth living.

Connect with Dr. Nelson on the following platforms:

Twitter: @kazjnelson

Facebook: @kazjnelson

Youtube: The Mind Deconstructed

Host: Madeline Ahern @maddie_ahern

Producer: Kirsi Oldenburg

Artwork & Video: Saurin Kantesaria

Music: Caligula - Windows96, Used with artist permission

01:13 Introduction - Dr. Kaz Nelson

06:20 Sacrifices of Physicians (and their families)

08:25 Limitations of the Mental Health System

13:00 AI Chatbots: Helpful or Harmful?

23:00 High-Acuity Care and Psychiatric Crises

30:30 First Interactions with the Mental Health System

37:10 The Mind Deconstructed

43:45 The Future of AI, Chat GPT

47:30 The Pull of the Status Quo

53:24 Advice to our Listeners

Dr. John Sargent: Dr. John Sargent is the co-founder of BroadReach Group and an internationally recognized thought leader who brings extensive experience in health systems strengthening large-scale patient education programs and the creation and implementation of public-private partnerships in emerging markets. Prior to co-founding BroadReach Group, he obtained his Doctor of Medicine from Harvard Medical School and gained experience as a strategic and operational consultant with expertise spanning multiple disease areas across public and private health sectors.

Annika Krugel: Annika Krugel is the Client Director for Vantage Health Technologies, which is a platform from BroadReach that uses AI to aggregate all data in an area or a clinic and then give individualized decision support, operational tools, and step-by-step workflows to empower healthcare workers. Annika has a Master’s Degree in Development Studies and has 15 years of experience across civil society and the public- and private sectors in various roles but always in a coordinating capacity.

1:15- Journey to the intersection of medicine and

9:00- Foundation of the BroadReach Group and current work

23:00- Inception of Vantage Health Technologies

25:20- Vantage Health Technologies’ role in the pandemic response

35:30- BroadReach Group and Vantage Health Technologies’ overall work across the globe

37:00- Future work

45:00- Future of big data and AI in medicine

50:30- Ensuring patient data privacy and security

52:30- Advice and ending thoughts

Ittai Dayan, MD is the co-founder and CEO of Rhino Health, a distributed computing platform leveraging privacy-preserving federated learning. The platform allows medical researchers and healthcare AI developers to seamlessly access diverse and disparate datasets and use them to create better AI algorithms.

contact@themamlpodcast.com

Host: David Wu / Twitter: @davidjhwu

Producer: Aaron Schumacher / Twitter: @a_schu95

Artwork & Video: Saurin Kantesaria

Music: Caligula - Windows96. Used with Artist Permission.

00:56 How did you come to the intersection of medicine and artificial intelligence?

06:15 What type of medicine did you start out studying?

11:35 Could you tell us the story behind Rhino Health?

14:30 What is federated learning?

21:00 Common use cases for Rhino Health?

26:45 Relationship between generalizability and accuracy when using federated learning?

28:15 What were your biggest challenges in creating Rhino Health?

32:40 An example of using Rhino Health?

37:40 How does Rhino Health integrate with EHR’s

38:15 What are your next steps for Rhino Health?

43:10 What do you think the future of AI in healthcare will look like?

48:08 What gives your life meaning and what are your greatest fears?

Beth Beadle, MD, PhD is the Director of Head & Neck Radiation Oncology at Stanford and co-creator of the Radiation Planning Assistant, a fully automated treatment planning assistant. 

00:56 How did you come to the intersection of medicine and AI?

03:20  What is radiation oncology and why does it fit well with AI?

05:54 How has radiation changed in your lifetime?

09:19 What is the Radiation Planning Assistant?

13:10 Describe radiation oncology workflow before the use of the radiation planning assistant

20:45  How does the model compare humans?

23:45 Where was the data source for the radiation Planning Assistant Model?

26:45 What safeguards exist for RPA?

28:00 What has been the response from other physicians, physicists, and patients to this model?

33:30 Timeline for implementation of the radiation planning assistant?

34:45 Background of the Radiation Planning Assistant

38:25 Future of Radiation Planning Assistant implementation

40:55 Adaptive planning in radiation oncology

43:15 Future of the Radiation Planning Assistant in the next 10 years

44:50 Are there any other projects being planned currently?

47:00 How has mentorship shaped your path?

50:25 What is the future of AI in medicine in 10-20 years

51:48 What advice would you tell yourself when you were graduating from medical school

53:20 What brings your life joy and meaning?

54:50 What are your greatest fears?

55:15 Favorite places to travel?

Dr. Tignanelli is the scientific director for the Program for Clinical Artificial Intelligence at the UMN Center for Learning Health Systems Science, the director of UMN Center for Quality Outcomes, and the chair of the Health Information Technology (HIT) Committee for the American College of Surgeons. Dr. Tignanelli's work serving COVID-19 patients during the pandemic and advancing AI research earned him the title of “Health Care Hero” by the Minneapolis/St. Paul Business Journal in 2021.

Follow us on Twitter @TheMaMLPodcast

Guest: Christopher Tignanelli, @cjtign

Host: Madeline Ahern, @maddie_ahern

Producer: Kirsi Oldenburg

Artwork: Saurin Kantesaria

Music: Caligula - Windows96: Used with artist permission

Notes:

01:00 tell us about yourself

04:00 critical care/acute care

06:30 COVID acute care

10:00 computer vision

14:00 rib fracture model

15:00 external vs internal validity

19:00 future of AI in medicine

Dr. Chenyang Xu is currently the President and Co-Chairman of PVmed Technologies, Co-Founding Partner of Silicon Valley Future Academy, Managing Partner of Brightway Future Capital, and an Advisory Board Member for the Johns Hopkins University BME department and formerly Advisory Board Member for the UC Berkeley EECS Department.

He was formerly the Chief Business Officer of RSP Systems and the GM and CTO of Siemens Technology to Business (TTB) at Berkeley where he led the Siemens’s North America’s technology startup partnership and early-stage investment practice out of the Silicon Valley. As former head of Siemens Interventional Imaging Program, he led an R&D team that developed over 10 new computer vision-based medical imaging products (e.g. CartoMerge) and has achieved billion dollar scale new revenue stream.

Follow us on Twitter! @TheMaMLPodcast

Guest: Chenyang Xu

Host: David Wu / Twitter: @davidjhwu

Producer: Aaron Schumacher / Twitter: @a_schu95

Artwork & Video: Saurin Kantesaria

Music: Caligula - Windows96. Used with Artist Permission.

01:00 - tell us about your path

11:00 - the internet + grad school in the 90s

22:00 - “career thinking” advice

30:30 - Siemens VC - looking at 10,000 startups

37:00 - PVMed - AI cancer treatment company in China

43:30 - AI startup ecosystem in China vs. US vs. Europe

56:30 - Potential downsides of implementing AI too quickly?

1:02:10 - Future of AI in medicine in 10-20 yrs?

1:13:00 - “Minority Report”-like AI to predict falls?

1:16:30 - Closing Questions

Akilesh Bapu is co-founder and CEO of DeepScribe, an AI-powered medical scribe that passively records and understands a patient’s visit, generates a clinical note, and then seamlessly inputs it into the electronic medical record.

Follow us on Twitter! @TheMaMLPodcast

Guest: Akilesh Bapu / Twitter: @AkileshBapu

Host: David Wu / Twitter: @davidjhwu

Producer: Aaron Schumacher / Twitter: @a_schu95

Artwork & Video: Saurin Kantesaria

Music: Caligula - Windows96. Used with Artist Permission.

1:10 - Pathway towards the intersection of medicine and Machine Learning

9:45 - What specialties are best fitted for DeepScribe implementation

11:55 - How does DeepScribe find relevant information for the clinical encounter

14:45 - The creation process of the DeepScribe hardware.

18:10 - DeepScribe’s ability to discern fragments of the SOAP note

19:45 - DeepScribe and billing concerns

24:25 - Scaling the human scribe capacity behind DeepScribe

29:45 - Implementing predictive models with DeepScribe

32:00 - Voice diagnostics

33:40 - Patient privacy and DeepScribe

38:55 - Vision for deep scribe

40:20 - Google Care Studio EHR mention

42:00 - Physician Burnout

42:35 - Advice for future founders

44:15 - What brings you joy?

45:35 - What are your greatest fears?

47:00 - What gives your life meaning?

50:25 - How do medical students think about the future of healthcare

Dr. Nigam Shah is Professor of Medicine (Biomedical Informatics) at Stanford Medicine and Chief Data Scientist at Stanford Healthcare. Dr Shah’s research focuses on combining machine learning and prior knowledge in medical ontologies to enable use cases of the learning health system.

Follow us on Twitter! @TheMaMLPodcast

Guest: Nigam Shah / Twitter: @DrNigam

Host: David Wu / Twitter: @davidjhwu

Producer: Aaron Schumacher / Twitter: @a_schu95

Artwork & Video: Saurin Kantesaria

Music: Caligula - Windows96. Used with Artist Permission.

TIMESTAMPS

0:45 tell us about your path and how you came to the intersection of MaML

4:15 what is the thesis of your work?

09:10 where is AI on the gartner hype curve

13:30 the equation of medicine - if risk > threshold, take action. Examples: GreenButton Consultation service + advanced care planning

27:00 how has mentorship shaped your path?

32:00 having fun in projects

34:00 “what makes a project fun?”

37:00 closing questions - what do you expect is the future of AI?

43:00 personal questions - what brings you joy?

Dr. Vineeta Agarwala is a general partner at a16z, physician, adjunct clinical professor at Stanford, previously at Google Ventures, Flatiron Health, having received her MD/PhD at Harvard Medical School and the Broad Institute. 

Many thanks to Ashlea Kosikowski from 1AB Media for making this episode happen!

Follow us on twitter: @TheMaMLPodcast

Host: David JH Wu @davidjhwu

Producer: Aaron Schumacher @a_schu95

Design: Saurin Kantesaria

Pat Walters is the Chief Data Officer and Patrick Riley is the senior VP of AI from Relay Therapeutics in Cambridge, MA. In this episode we discuss Relay's innovative approach to drug discovery, and how new developments in AI and computational modeling have accelerated this process.

Matt Diamond, MD, PhD, is Chief Medical Director of the FDA’s Digital Health Center of Excellence. Dr. Diamond provides leadership for digital health policy development and implementation for emerging technologies including artificial intelligence. Prior to joining the Agency, Dr. Diamond served on leadership teams of large and small technology companies, including as Chief Medical Officer at Nokia, and as Medical Director at Fossil Group. He earned his MD and PhD (biophysics) from the Mount Sinai School of Medicine, and is board certified in rehabilitation medicine and sports medicine and certified in medical acupuncture.

TIMESTAMPS

1:15 tell us about your path

5:15 what’s a typical day like

8:26 how would you define a digital health technology?

10:50 regulation of medical devices is based on their intended use

13:00 are AI technologies fundamentally different from a stethoscope

19:30 talking about new FDA guidelines - Digital Health Technologies for Remote Data Acquisition in Clinical Investigations

27:00 collaborative communities (join one!)

30:00 FDA & relationships with companies

37:00 what do you think the future of AI in medicine will look like?

39:55 what advice would you give yourself

Contact us! contact@themamlpodcast.com

Host: David Wu / Twitter: @davidjhwu

Producer: Aaron Schumacher / Twitter: @a_schu95

Artwork & Video: Saurin Kantesaria

Music: Caligula - Windows96. Used with Artist Permission.

Joy Kincaid is our guest today from OncoHealth, which has just launched their new digital telehealth platform supporting cancer patients and their families. Joy was formerly VP of Population Health at Optum.

1:06 “So our first question is.. tell us about your path”

7:25 talking about Optum

11:50 OncoHealth

13:48 Iris Digital Health platform

37:35 Talking about the Healthcare Industry

45:56 future of digital medicine in 10-20 years?

48:40 “the movement of the Tao is returning”

51:45 advice to recent grads

53:30 intentional networking

57:50 what gives your life meaning?

Host: David Wu / Twitter: @davidjhwu

Producer: Aaron Schumacher / Twitter: @a_schu95

Artwork & Video: Saurin Kantesaria

Description:  Neal Khosla is the founder and CEO of Curai Health, a digital health startup making big waves in the primary care space. To learn more, please visit curaihealth.com.  

1:00 Tell us about your path and how you came to the intersection of medicine and machine learning

13:15 - Most common patient demographic served

15:45 - Story of how Curai began. + history of medicine

29:00 - Can med + AI reason better than us? + vision for Curai

40:30 - When should patients go into clinic?

44:40 How has regulation affected you 

47:00 - How has mentorship shaped your path

53:20 - 3 questions i want to ask everyone

What are you most afraid of?

What do you believe in?

What gives you strength?

58:00- What do you want from the universe?

Intro Music - Windows96 - Caligula (song used with permission from artist).  

Host: David Wu @davidjhwu 

Producer: Aaron Schumacher @a_schu95 

Cover Art: Saurin Kantesaria  

Follow us on twitter @themamlpodcast Email us! contact@themamlpodcast.com Looking for industry sponsors!

"Because Life needs Art to explicate its meaning,

and Art needs Technology to keep its edge bleeding."

Yvonne Lui, MD, is the associate chair of radiology of NYU Langone Health. Today we discuss FastMRI, the interesting collaboration between NYU and Facebook AI. We explore how machine learning can enhance image reconstruction following an MRI scan. 

0:00 Background on MRI

2:36 Intersection of Medicine and Machine Learning for Dr. Lui

6:40 Current Research Projects 

11:40 MRI partnership with Facebook AI & MRI Image reconstruction 

17:12 Models for research in clinical trials

21:30 Why Facebook is interested in this problem

24:15 Further information on the Machine Learning MRI reconstruction

27:45 Academic & Industry Collaboration 

35:00 Hesitancy in collaborating with industry

37:05 What areas of AI are Dr. Lui interested in 

38:45 Future of AI and Medicine

40:30 Radiology and Automation

43:00 Dr. Lui's balance between clinical work, research, and administrative work

48:20 What advice would you give to yourself early on in your career. 

Host: David Wu @davidjhwu

Producer: Aaron Schumacher @a_schu95

Cover Art: Saurin Kantesaria

David Lindsay is the CEO of Oncora, a data, documentation, and personalized care solution for specialty oncology. Oncora is currently implemented in many major hospital systems, including Northwell Health, MD Anderson, Mass General Children's, and Scripps Health. David founded the company during his MD/Ph.D. training at the University of Pennsylvania.

0:00 Introduction

1:15 David Lindsay tells his story

5:00 Initial Idea for a company bringing AI to oncology

7:35 When did David decide to start the company

10:25 Balancing being a CEO and a medical student at the same time

12:30 The early challenges in starting the company

14:30 The initial offerings of the company

17:50 Obtaining initial data sets

20:25 Initial funding for the company

24:45 A use case to predict hospitalization

27:10 How are these AI technologies regulated

30:00 How do clients pay for this technology

32:45 Tracking quality metrics

36:00 Integrating with an EHR

37:30 Further development of the product

41:35 Next steps for Oncora

43:55 Industry vs Academia

47:30 Next for AI in 10 - 20 years

48:20 Advice for yourself 20 years ago

50:00 Advice for students in industry

53:30 David’s plans for the future

Host: David Wu @davidjhwu

Producer: Aaron Schumacher @a_schu95

Cover Art: Saurin Kantesaria

Carmen Aguirre is a 4th-year medical student, visual jockey (VJ), and NFT artist. Here we sit down and discover what pushes her to continue developing her passions for medicine and artificial intelligence. You can find more info about her work here: https://linktr.ee/Neurite 0:00 Alex gives some background on NFT's 

2:50 Background and Initial Involvement with NFT's 

5:50 Seeing stigma around mental health conditions 

9:10 Building a community around NFT's 

9:40 The process of creating NFT's 

13:20 Working for Ariana Grande 

15:10 Working as a DJ and in music while in medical school 

21:10 "Work is my life" - how Carmen balances the commitments 

22:10 Time management 

24:35 How a creative outlet benefited Carmen's mental health 

27:25 Raising Money for mental health charities through NFT's 

30:30 Going from animation to the drop process 

34:00 Return of live music, graduating medical school and residency 

36:45 The average medical student compared to the average 24-year old musician 

39:15 How will technology change medicine in 10-20 years 

41:00 Advice you would have given yourself going into your 20's

Interviewer: David Wu

Producer: Aaron Schumacher

Art: Saurin Kantesaria

In our first ever two-interviewee episode we welcome Doctors Shannon Haymond and Christopher McCudden.

Dr. Haymond is the Vice Chair for Computational Pathology and Director of Mass Spectrometry at Lurie Children’s hospital of Chicago and an Associate Professor of Pathology at Northwestern University Feinberg School of Medicine.

Dr. McCudden is the Vice Chair for the Department of Pathology & Laboratory Medicine at the University of Ottawa and a Clinical Biochemist in the Division of Biochemistry at The Ottawa Hospital.

They are co-authors of "Rise of the Machines: Artificial Intelligence and the Clinical Laboratory." This article details the potential uses for artificial intelligence within the clinical laboratory. From newborn screening and inborn errors of metabolism to toxicology screens and everything in between, this article not only provides insight into the future of artificial intelligence, but also a peek into the clinical laboratory.

In this interview, we talk about the many paths to understanding and working with machine learning, from Dr. Haymond who is classically trained, to Dr. McCudden, who taught himself "R." We discuss the use of mass spectrometry, genomics screenings, and other current laboratory techniques and how they might be aided by artificial intelligence.

We hope that this interview gives you a comprehensive look into the world of laboratory medicine that is at the heart of all healthcare systems. Thank you and enjoy!

1:37 What are Clinical Laboratories?

3:20 Breaking up with Excel - Dr. Haymond’s Journey

5:49 All Specialties use the Lab

7:40 Learning “R” - Dr. McCudden’s Journey

11:31 Clinical Mass Spectrometry

15:45 Newborn Screening and Genetic Testing

17:41 Clinical Biochemistry

21:56 Artificial General, Narrow, and Super Intelligence

23:40 AI in Genomics

29:55 Open Source or Proprietary?

32:33 Future of AI In the Clinical Lab

38:50 Advice to Listeners

Shanen Boettcher is a former general manager at Microsoft, product manager at Netscape, and now a PhD student at the University of Saint Andrews currently studying AI Ethics and Spirituality.

Shanen was recently featured in a New York Times Article titled, "Can Silicon Valley Find God?" He is a pioneer in this field and deftly explores two very disparate topics to deeper probe the pressing questions of our generation. 

In this interview we discuss how artificial intelligence can facilitate positive conversations about faith, the impact of faith/spirituality on health, as well as other interesting topics like the study of world religions and spiritual texts. We close with some advice for individuals looking to get involved in this sort of work. 

1:20 Introduction and Background

2:55 Why study world religions?

5:50 Looking back on the days at Microsoft

7:20 The research that led to a New York Times article

11:55 Should AI expose people to their existing religious beliefs or provide new perspectives from other religions.

17:00 Where does this research go from here?

20:10 Do interactions with AI have an impact on people’s religious beliefs?

27:18 Use of algorithms for answering existential questions

30:00 How can this research help people?

37:10 Preventing religious bias in AI systems

40:20 Will technology bring us closer or further from our spirituality?

49:55 What should AI say when you ask existential questions?

52:55 Does the source of the voice affect an individual's interpretation of the answer?

1:02:50 Advice for those in their 20’s

1:07:30 Do we need religion going forward?

1:13:45 A favorite spiritual text

Dr. James Zou of Stanford University is an inaugural Chan-Zuckerberg investigator and faculty director for the university-wide AI for Health program.  

Dr. Zou recently published a paper in Nature which is making waves in the clinical trial world because it is causing us to rethink how we set eligibility criteria for clinical trials. Using an ML approach, he shows that by changing such criteria, we can make trials both more inclusive, opening them up to way more patients, while at the same time safeguarding patient safety. 

We also talk about his various other research projects, which span the gamut from evaluating FDA approvals of AI algorithms, all the way to deeper mathematical concepts like data valuation. Dr Zou is an impressive titan in the AI and medicine  space. In this interview I really came to appreciate how broad his research spans, which I think is key to his many successful projects. We ultimately close with some good advice for people looking to get involved in this exciting and growing space. 

02:45 Introduction to the intersection of Medicine and AI

4:20 Life after Ph.D.

6:35 New Nature paper on AI and clinical trials

13:25 How did we approach this question?

14:15 Data Driven Approach - Trial Path Finder

16:59 The ethical implications of this approach

19:35 Why are minority populations excluded from research?

20:25 Using AI to include ineligible patients in clinical trials

23:50 Future for this project

27:00 Evaluation of FDA approvals for AI algorithms

32:23 Favorite Project Dr. Zou has worked on

35:01 Dr. Zou's favorite math concept in the machine learning space

39:10 Separating signal from noise

39:53 Dream research projects

41:00 Future of Ai % medicine in 10-20 years

43:15 The human and AI team

47:40 What advice would you give to your 20 year old self

Interviewer: David Wu

Producer: Aaron Schumacher & Alexander Jacobs

Art: Melanie Bussan

Follow us on Twitter: 

https://twitter.com/TheMaMLPodcast?s=20

In this episode we discuss a novel idea in the healthcare and AI space: using Swarm Learning and blockchain technology for decentralized and confidential machine learning on clinical data. This promising new framework for collaborative research improves both algorithm performance and preserves patient privacy. 

This idea has been pioneered by Dr. Joachim Schultze, who recently published an exciting new paper on the subject in Nature. Dr. Joachim Schultze is a professor of Genomics and Immunoregulation at the DZNE in Germany and the University of Bonn.

2:30 Introduction and Background

6:00 Studying Broadly as an Academic

9:10 Joachim Schultze's introduction to A.I. through work on Leukemia

14:45 Recent Nature Paper - Swarm Learning & The Blockchain

21:10 Federated Learning vs. Swarm Learning

23:00 Using the Blockchain and Smart Contracts to Secure Data Sets

25:00 External Threats to the Swarm

29:40 Reaching Agreement Before Inter-Institutional Swarm Learning

35:50 Utilizing Multiple Nodes to Answer a Clinical Question

39:18 Reducing Technology-Driven Noise and Decreasing Bias With the Swarm

44:50 Open Science, Open Insights, But is Open Data Absolutely Necessary?

46:46 The Necessity of an Interprofessional Team to Complete This Project

48:30 Next Steps For This Project

54:10 Central Maintenance For The Swarm

55:10 Future of A.I. in Medicine

59:40 What Advice Would You Give To Your 20-Year Old Self

Interviewer: David Wu

Producer: Aaron Schumacher & Alexander Jacobs

Art: Saurin Kantesaria @saorange314 - Instagram

Dr. Vivian Lee, MD, Ph.D., MBA is currently President of Health Platforms at Verily, an Alphabet Company. Dr. Vivian Lee is also the author of the latest book “The Long Fix,” a book about solving America’s healthcare crisis.

Dr. Lee has accomplished much in her diverse career. She received a doctorate in medical engineering from Oxford as a Rhodes scholar, her MD from Harvard Medical School, was valedictorian at NYU Stern School of Business, authored over 200 peer-reviewed research publications, as well as a cardiovascular MRI textbook, former CEO of the University of Utah Health and dean of their medical school and, is now the President of health platforms at Verily Life sciences, an Alphabet company.

In this interview, we talk about her journey to Verily today and her thoughts on how healthcare has been changed for the better by new technologies like Digital Health Platforms, an example being Onduo for blood glucose management in diabetics.

We also talk about how medicine has changed from the days she started medical school to the future landscape that current medical students face today, one that is much more integrated with payers, tech, politics, and employers. We hope that this interview inspires you as it did to us to try and tackle all of healthcare’s problems with renewed vigor. Thank you and enjoy!

P.S. Please check out Dr. Vivian Lee’s latest book “The Long Fix” and review it on Amazon/GoodReads!!

2:50 Dr. Vivian Lee’s journey

8:20 Transition to Radiology

12:40 Transition to Univ. of Utah

15:50 “What does your job at Verily entail?”

17:10 Onduo - an example of Health Platforms in action

23:50 Verily and COVID testing

27:40 “The Long Fix” and the Co-Production of Health

37:00 MedSchool now vs. MedSchool then

44:00 Verily and how it affects the future of medicine

46:00 David’s misattributed Luddite fears

50:00 What advice would you give your younger self?

Interviewer: David Wu @davidjhwu - Twitter

Producer: Aaron Schumacher @a_schu95 - Twitter

Art: Saurin Kantesaria @@saorange314 - Instagram

Dr. Nneka Comfere is a Dermatologist and Dermopathologist at the Mayo Clinic in Rochester, MN. We discuss Dr. Comfere's discovery of visual beauty within dermatology and how this can be applicable in a machine learning setting. We also talk about the possible uses for dermatoscopes and artificial intelligence to fill gaps in care based on location. Dr. Comfere's take on AI from a clinician's perspective is accessible to not only medical professionals, but also those seeking to learn more about how machines are becoming part of the healthcare system. Enjoy!

Interviewer: Maddie Ahern

0:25 - Journey to Dermatology and Dermopathology, Integration of AI

9:35 - Articles in Journal the American Academy of Dermatology

19:25 - Initial Venture into AI, Building a Dermatological Database

28:32 - Future of AI in Medicine

32:15 - What is Next for Dr. Comfere?

36:51 - Advice for Students/Learners

Ian Pan, MD, is a Kaggle Grandmaster, radiologist resident at the Brigham and Women’s Hospital, and a rising star in the medicine and AI field.

Kaggle competitions are international data science competitions that are both very competitive and prestigious. We talk about Ian's path to medicine and AI as well as the various strategies he’s used to become one of the top coders globally in this burgeoning new field. Ian also gives some great advice on how to get started and we close with some of his exhortations against poor practices in ML today.

This interview was a lot of fun and if you are curious about Kaggle competitions or how to be the best at them, this interview is for you.

Time-Stamps

• 6:20 Initial interests in Radiology
• 12:50 2018 Pneumonia detection Kaggle challenge
• 17:55 Domain expertise not necessary for AI learning
• 20:18 How to approach an AI challenge
• 25:23 The structure of Ian's Kaggle-winning models 
• 28:58 What sets Ian's models apart
• 32:30 Non-medicine endeavors 
• 37:40 Coding Background
• 39:00 Should medical students learn to code
• 42:00 The future of AI in medicine
• 49:10 What’s next for Ian
• 54:07 Necessary changes to AI in medicine
• 57:15 Advice for medical students
  

Anouk Stein, MD, is a radiologist and AI Data Specialist at MD.ai, a healthcare start-up based in NY. We discuss Dr. Stein's journey to MD.ai as well as her current work in the medical AI space. Dr. Stein provides some great advice for anyone looking to get started in practical machine learning. We also talk about some of the exciting kaggle competitions held by MD.ai as well as the importance of external data validation. We close with some great advice for our listeners from Dr. Stein on how to embrace the exciting new changes taking place in medicine today. Dr. Stein is a terrific teacher and I learned a lot from her in this interview. I hope you all enjoy!

Timestamps 

1:10 Individual path to healthcare and AI 

3:58 What does MD.ai do? 

5:00 Stanford Design-Your-Life course 

7:40 AI and Radiologists 

12:00 Combining algorithms and the necessity of a meta-algorithm 

14:48 External Validation of Data  

16:50 Practical Machine Learning 

22:10 What is external validation  

25:10 Controversy over generalization  

29.10 Accomplishments of MD.ai 

32:30 What is it like to work in industry after medical education  

37:45 How MD.AI got started  

40:25 Fields of Medicine looking towards AI 

43:40 The future of AI in medicine over the next 10 - 20 years  

46:25 What advice would you give to yourself in your twenties  

47:00 Any advice for young physicians

Resources Mentioned:

• Kaggle - Python tutorial
  
• Fast.ai
  
• Facebook Detectron 2
  
• Pandas
  

Judy Gichoya, MD, MS, is an interventional radiologist at Emory University in Atlanta. We begin by talking about Dr Gichoya’s early days in Kenya where she participated in building OPEN MRS, the world’s leading open-source EMR platform. We then talk about her work in using AI to combat bias and social injustices in medicine and the importance of diversifying the datasets we use in AI work today.

03:00 Origins in Kenya, building OpenMRS, path to AI

14:00 Research topics of interest in the Gichoya Lab (Emory) such as bias in AI

21:00 steps we can take to combat bias in datasets

27:00 work on federated learning

36:00 advice to medical students / early-career med students interested in the field

43:00 balancing clinical work and informatics research

50:00 favorite food from hometown!

Daniel Tse, MD, is a product manager at Google Health. In this episode, we talk about his unconventional path to healthcare and AI, and discuss the career angst that many may feel when they wonder that if the path they are on is the right path. I can’t say that we’ve discovered the perfect answer in this interview, but I will say that Dr. Tse is someone who has thought deeply about this question as you can see from his unique career and will probably provide some good words of advice.

Had a real pleasure recording this interview. Hope you all enjoy!

Interviewer: David Wu

Producer: Aaron Schumacher

1:30 Childhood + Budding Interest in Computers

8:00 Path to medicine

12:00 Working at a healthcare startup during medical school

20:00 Working at Google after graduating medical school

30:00 Comparing and contrasting Google with an academic research lab

35:00 MD training + how it relates to working at Google

38:00 What does a product manager do?

45:00 What do you expect is the future of AI + medicine?

49:00 What advice would you give to your 25 yr old self?

52:00 Advice to people who are questioning whether medicine is the right path for them

Jack Moore is a product manager at Qventus, a healthcare start-up based out of Mountain View, California. In this episode, we go into depth about how he and his team are using machine learning to improve the efficiency of hospitals such as the University of Minnesota’s MHealth system. One example we discuss is how Qventus builds and deploys models to improve patient care and reduce ICU bed wait times. At the end, we spend time talking about bias and race in medicine and how we can use AI to combat the propagation of such biases.

Interviewer: David Wu

Producer: Aaron Schumacher

Time Stamps:

Path to healthcare AI and Qventus: 01:00

What does Qventus do 04:15

Story behind Qventus 09:00

What if the AI model and physicians disagree? 15:00

Structure of the Qventus AI models + explainability 21:00

Integration with Epic 30:00

What Qventus use looks like as a provider 36:00

Cool definition of data science 42:00

AI to reduce health inequalities and racial bias in medicine 58:00

Miguel Alvarado is the CTO of Lumiata, a healthcare AI company based in SF. In this episode, we talk about Miguel’s unconventional journey from Microsoft in the 90s all the way up until the present moment where he is helping bring the power of big data and AI to patients, providers, and insurers.

Just like Bruce Wayne, Miguel works during the day, but at night, has an alter ego. we will talk about his passion as an underground house and techno DJ and finally, we will close with a conversation about the importance of being present.

I had a real pleasure conducting this interview with Miguel and I hope you enjoy it.

00:00 Intro

02:00 Beginnings

12:30 What does Lumiata do

23:40 Synthetic Data

30:00 Explainability in models

34:00 what do you think is the future of AI in medicine

40:00 the importance of being present

44:00 the importance of having other passions

51:00 dj’ing, techno, and being fully present

55:00 machine learning and.. music?!

Welcome to the very first episode of the Medicine and Machine Learning Podcast, AKA the MaML Podcast brought to you by the Medicine and Machine Learning Club of the University of Minnesota Medical School. We are a group of students passionate about learning more about the future of medicine and AI. This Podcast will feature interviews from prominent figures in academia, industry, and medicine exploring the cutting edge of healthcare’s newest frontier: medicine & machine learning.

My guest today is Dr. Christopher Weight, the new Director of Urologic Oncology at the Cleveland Clinic. In addition to this role, Dr. Weight is currently helping build a new center for AI and Medicine at the Cleveland Clinic. In this episode, we will be talking about his unique path in medicine and how it led to building one of the largest and most comprehensive kidney cancer imaging datasets we have today.