Proud moment for our lab at #SIIM26! 🎉
Congratulations to our wonderful PI, Dr. @Slowvak, on receiving the Dr. Joe Gitlin Award, and to Dr. @BrdKhsrv on receiving the Early Career Award.
Our puzzle on #NVIDIA A100 vs. H100 hardware nuances in medical AI is being displayed at the #SIIM26 Case-of-the-day booth! 🚀
The Challenge: Check out the screenshot in the photos below👇
Official solution drops by Friday. Let's see who gets it right!
@MayoAILab
Bro, I can’t wait to make it.
I get many stories to tell. Advice full my mouth too, but e go be like ramblings of a mad man for now, because ROE (Return on Efforts) no dey.
The biggest cheat code on the planet is internalizing that nobody cares about your life...People think about you for one second and then think about themselves again. So you might as well do what you want.
👉 Join our next #MICCAI Industry Talk to see how the SHIP team at IKIM, University Hospital Essen, built a whole-body organ analysis tool using deep learning + generative #AI. Includes a live demo.
Register now
🔗 https://t.co/oEmSs24tyL
@MiccaiStudents@WomenInMICCAI@RMiccai
One common issue with personalization in all LLMs is how distracting memory seems to be for the models. A single question from 2 months ago about some topic can keep coming up as some kind of a deep interest of mine with undue mentions in perpetuity. Some kind of trying too hard.
@ElisonLB1@Nigerian_Doctor try watching sketchy videos. it really helps with memorization. You can also check out the "Microbiology Made Ridiculously Simple" book.
You can DM if you need access to any of these resources.
Announcing my new course: Agentic AI!
Building AI agents is one of the most in-demand skills in the job market. This course, available now at https://t.co/zGHUh1loPO, teaches you how.
You'll learn to implement four key agentic design patterns:
- Reflection, in which an agent examines its own output and figures out how to improve it
- Tool use, in which an LLM-driven application decides which functions to call to carry out web search, access calendars, send email, write code, etc.
- Planning, where you'll use an LLM to decide how to break down a task into sub-tasks for execution, and
- Multi-agent collaboration, in which you build multiple specialized agents — much like how a company might hire multiple employees — to perform a complex task
You'll also learn to take a complex application and systematically decompose it into a sequence of tasks to implement using these design patterns.
But here's what I think is the most important part of this course: Having worked with many teams on AI agents, I've found that the single biggest predictor of whether someone executes well is their ability to drive a disciplined process for evals and error analysis. In this course, you'll learn how to do this, so you can efficiently home in on which components to improve in a complex agentic workflow. Instead of guessing what to work on, you'll let evals data guide you. This will put you significantly ahead of the game compared to the vast majority of teams building agents.
Together, we'll build a deep research agent that searches, synthesizes, and reports, using all of these agentic design patterns and best practices.
This self-paced course is taught in a vendor neutral way, using raw Python - without hiding details in a framework. You'll see how each step works, and learn the core concepts that you can then implement using any popular agentic AI framework, or using no framework. The only prerequisite is familiarity with Python, though knowing a bit about LLMs helps.
Come join me, and let's build some agentic AI systems!
Sign up to get started: https://t.co/FX35dloqw4
USMLE Tip: Parvovirus B19 causes an aplastic *crisis*, which is a sudden, severe anemia due to temporary suppression of red blood cell production. It does not cause aplastic *anemia*, which is defined by a hypocellular marrow with pancytopenia. Parvovirus only affects red cell precursors and does not cause global marrow failure.
Medical imaging AI needs serious rethinking. My keynote speech to @MICCAI_Society Annual Conference #HECKTOR Challenge: "Turning Medical Imaging AI Breakthroughs into Bedside Impact."
https://t.co/7tiKi1LANq
Also, is it Physician-in-the-loop AI or #AI-in-the-loop Physician?