
cancer risk prediction, LLMs in Medicine, AI in GI || AI researcher and Gastroenterologist || @DOMSinaiNYC || @bronfman_inst || Views are my own
Manhattan, NY
Joined August 2020
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🚨 Application deadline extended to July 18!
The ASGE AI Scholars Program offers a fully funded year of AI training for aspiring GI leaders. Get personalized mentorship, attend exclusive workshops, and contribute to a dynamic team project.
Apply now: tinyurl.com/AIScholars2025
📣 Applications for the 2026 @ASGEendoscopy AI Scholars Program are OPEN!
Over 2 years, it's helped GI fellows & faculty deepen their AI expertise and launch projects with lasting impact. Know a future leader in AI?
⏰ Deadline June 30. Apply or tag someone who fits!
#AIinGI
Now is your chance to join an exclusive group of AI Scholars collaborating with world-renowned mentors on groundbreaking projects driving the next wave of AI-powered care, research and discovery. Applications now open through June 30. hubs.ly/Q04m3xf50
Pictured AI Scholars Class of 2025/2026
#AI #AIGI #GIInnovation #GITwitter
Ali Soroush, MD, MS retweeted
One of my former research assistants was killed by ICE in Minneapolis today😢
Alex was the kindest, sweetest human and a ICU nurse with a bright future ahead of him
May his soul rest in peace and this senseless carnage stop💔
Ali Soroush, MD, MS retweeted
💡 Innovate in GI with #AI! Join the 7th Global GI & AI Summit Sept. 13 and hands-on workshop Sept. 12 in D.C. Learn from Drs. Sharma, Wallace, Appalaneni & Gupta. Explore AI tools, case demos and strategies to boost patient care.
🔗 hubs.ly/Q03FTMzl0 #GITwitter #AIinGI
Ali Soroush, MD, MS retweeted
Ali Soroush, MD, at Mount Sinai is leading efforts to apply generative AI in gastroenterology, from automating EHR data extraction to improving cancer screening.
Learn how AI is transforming GI care and research: bit.ly/4kLpWM9
@MountSinaiGI @AliSoroushMD
Ali Soroush, MD, MS retweeted
I’d like to share a tip for getting more practice building with AI — that is, either using AI building blocks to build applications or using AI coding assistance to create powerful applications quickly: If you find yourself with only limited time to build, reduce the scope of your project until you can build something in whatever time you do have.
If you have only an hour, find a small component of an idea that you're excited about that you can build in an hour. With modern coding assistants like Anthropic’s Claude Code (my favorite dev tool right now), you might be surprised at how much you can do even in short periods of time! This gets you going, and you can always continue the project later.
To become good at building with AI, most people must (i) learn relevant techniques, for example by taking online AI courses, and (ii) practice building. I know developers who noodle on ideas for months without actually building anything — I’ve done this too! — because we feel we don’t have time to get started. If you find yourself in this position, I encourage you to keep cutting the initial project scope until you identify a small component you can build right away.
Let me illustrate with an example — one of my many small, fun weekend projects that might never go anywhere, but that I’m glad I did.
Here’s the idea: Many people fear public speaking. And public speaking is challenging to practice, because it's hard to organize an audience. So I thought it would be interesting to build an audience simulator to provide a digital audience of dozens to hundreds of virtual people on a computer monitor and let a user practice by speaking to them.
One Saturday afternoon, I found myself in a coffee shop with a couple of hours to spare and decided to give the audience simulator a shot. My familiarity with graphics coding is limited, so instead of building a complex simulator of a large audience and writing AI software to simulate appropriate audience responses, I decided to cut scope significantly to (a) simulating an audience of one person (which I could replicate later to simulate N persons), (b) omitting AI and letting a human operator manually select the reaction of the simulated audience (similar to Wizard of Oz prototyping), and (c) implementing the graphics using a simple 2D avatar.
Using a mix of several coding assistants, I built a basic version in the time I had. The avatar could move subtly and blink, but otherwise it used basic graphics. Even though it fell far short of a sophisticated audience simulator, I am glad I built this. In addition to moving the project forward and letting me explore different designs, it advanced my knowledge of basic graphics. Further, having this crude prototype to show friends helped me get user feedback that shaped my views on the product idea.
I have on my laptop a list of ideas of things that I think would be interesting to build. Most of them would take much longer than the handful of hours I might have to try something on a given day, but by cutting their scope, I can get going, and the initial progress on a project helps me decide if it’s worth further investment. As a bonus, hacking on a wide variety of applications helps me practice a wide range of skills. But most importantly, this gets an idea out of my head and potentially in front of prospective users for feedback that lets the project move faster.
[Original text: deeplearning.ai/the-batch/is… ]
🧵 Can we unify upper GI cancer screening with cumulative risk prediction?
At #DDW2025, Alyssa Chen (PGY2!) presented our proof-of-concept risk model for predicting 5-year UGI cancer risk, using only readily accessible EHR data.
Read more👇1/
Check out these cool AI posters showcasing our team's work today at #DDW2025!
Sa2035: MERGING INFLAMMATORY BOWEL DISEASE GUIDELINES USING LARGE LANGUAGE MODELS AND RETRIEVAL AUGMENTED GENERATION
1/
Sa2075: ZERO-SHOT VISION-LANGUAGE MODELS COMPETE WITH TRAINED MACHINE-LEARNING MODELS ON POLYP DETECTION AND CLASSIFICATION
2/
Limitations:
- Retrospective, single-center
- Missingness, errors in EHR data
- Small number of biopsy-confirmed events
We’re addressing this and more through:
📈 Additional modeling methods
🧠 LLM data extraction
🌍 External validation
👥 Algorithmic fairness metrics 10/
Thank you to:
- the the entire study team for their efforts: Suraj Jaladakani @pushkala2307 Ji Yoon Yoon Chin Hur @proftatonetti @girish_nadkarni.
- @AGA_Gastro for their critical research support. 11/








