Machine Learning for MDs Weekly Digest
The mission of ML for MDs is to connect physicians interested in machine learning. This newsletter provides learnings at the intersection of medicine and machine learning.
Fun Fact
- In 2017, Facebook programmed two AI chatbots to converse and learn how to negotiate, but as they went back and forth they ended up forgoing English and developing their own language, completely autonomously.
Weekly Summary
This week I’ve compiled a list of resources, vetted by the ML for MDs community, for those new(ish) to the field of healthcare AI. Many thanks to all the awesome members who have suggested videos, courses, textbooks, etc.
Healthcare AI Resources
Where to Start
I often get questions about where to start with ML/AI. It’s a big and technical field, so I put together a list of some of the group’s favorites for folks to work through. Some of the best resources have been compiled by a16z in their AI Canon, which I highly recommend as a starting place. They highlight intro to expert references, as well as commentary, landmark papers, a market overview, and practical tools for building products. I’ve added and highlighted some of my personal favorites, but it’s hard to go wrong if you work your way through their suggestions.
Here I’ve focused more on:
- Healthcare AI-specific resources
- Textbooks (we got so good at learning via textbooks in med school!)
- Low/no code resources recommended by the community not listed in the a16z document
- Low-commitment ways to stay up to date on the latest healthcare AI developments
The usual caveats apply:
- I’m sure there are amazing resources I’m not aware of/didn’t include. People are producing new, amazing content every day
- This list is aimed at physicians interested in getting a better technical understanding of deep learning and AI, not necessarily clinical use
- The field is changing quickly and some aspects of some of the courses may not have the newest advances
- These resources are focused more on understanding the technology; there are emerging fields that focus solely on the implementation/limitations/bias/etc that aren’t covered in detail here (yet!)
If you’re new to the field and it all seems like too much
- Read the two papers I list below for the quick intro
- Read 2-3 of the “gentle introduction” links from the a16z document
- Complete one of the short video courses below
- Sign up for 2-3 of the medical and/or general newsletters listed below.
- Figure out what your specific interests are
- Look at the resources on a16z or here to dive deeper into those interests
For people who want a quick background:
A short guide for medical professionals in the era of artificial intelligence – written in 2020 so doesn’t cover some of the newest advances, but is an easy read with great graphics to understand the kinds of AI, kinds of learning, and high-level ideas on how to evaluate AI papers.
A Surgeon’s Guide to Machine Learning – published in 2021, this paper covers some of the practical aspects that a physician (not just a surgeon) might encounter when trying to develop a study using machine learning: data manipulation/preparation, choosing a model, and evaluating model performance
Short, free video intro courses:
MIT Introduction to Deep Learning: Videos of a compressed one week intro to ML class at MIT. It’s a really nicely explained, concise overview of ML with some interesting guest speakers toward the end.
Google’s Generative AI Learning Path – 10 courses that can be taken individually or separately, each about 45 min. Lets you “play” in the Google generative AI studio at the end.
Deeplearning.ai courses on Coursera – Free series of courses taught by Andrew Ng, very well-reviewed overall
Longer but excellent free courses:
MIT’s Machine Learning for Healthcare – 25 lectures with associated slides/pdfs about fundamental issues in the field. No videos.
Harvard’s CS197 – AI Research Experiences – More advanced course with a textbook and lecture notes, great resources and fun layout. No videos.
MIT’s Linear Algebra – Deep dive videos on linear algebra (which underpins much of ML) in a fun way (yes, fun!).
Udemy Machine Learning A-Z: Very user-friendly introduction to ML, probably need a small amount of programming experience but not much. Can use Python or R. About $20.
Free e-books for people comfortable with math and/or programming:
The Little Book of Deep Learning – free pdf explaining deep learning concepts. Heavy on the math but concise and has some good visuals to support the equations
Langchain AI ebook – From Pinecone, great free practical coding resource, definitely need a coding background
More general healthtech resources:
Sam Altman’s Startup school – great free videos from the original Stanford lecture series and other resources
Y Combinator Startup Library – Great easy to read resources on business topics we weren’t taught in medical school
a16z list of healthtech companies – a nice list of companies and what they do. Worth checking out if you have an interest in a specific area of digital health to see (some of) what’s already out there
Medical AI news
Doctor Penguin – Great medical AI newsletter, also from Eric Topol
Ground Truths Substack by Eric Topol: Covers medical innovation, often including AI/ML topics
Getting Clinical Substack from ScienceIO– Health tech, policy, and AI
General AI news
TLDR AI-Up to the minute news about AI
AI Explained – YouTube channel about new developments in AI
Super Data Science Podcast: A wide variety of topics including several with a medical focus
Towards Data Science – On Medium (so only a few articles/month if not a member), but has some great explanations of data science concepts
Machine Learning Mastery – Also great intro to ML concepts in blog posts that are very readable and have great visuals
XPC – Great resource for primary care-related health tech news
Communities
ML for MDs – all-physician group focused on impact of ML/AI in healthcare. Amazing group of tech-savvy physicians
Health Tech Nerds – many people in the digital health space – majority non-clinical. $20/month or $200/year
Textbooks
The AI Revolution in Medicine – Not really a textbook, but an overview of the field by some major names including Peter Lee and Isaac Kohane
Intro to Statistical Learning: Basic ML introductory textbook
Deep Learning with Python by Chollet: A great, very readable textbook for those with some Python background
Hands on Machine Learning: More in-depth ML textbook
These are all available at mlformds.com/resources for easy reference.
Community News
- If you haven’t introduced yourself, please do so under the #intros channel.
Thanks for being a part of this community! As always, please let me know if you have questions/ideas/feedback.
Sarah
Sarah Gebauer, MD