Christian Bluethgen
@cxbln.bsky.social
470 followers 280 following 49 posts
Radiologist @ USZ Zurich | Med AI research @ Stanford AIMI | #RadSky #ChestRad 🫁🫀 🩻
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cxbln.bsky.social
A small starter pack for accounts in the radiology/AI space (or prolific in either of those fields) - happy to add more! go.bsky.app/9Drtasz #RadSky #Radiology #AI
Reposted by Christian Bluethgen
iamjbd.bsky.social
💥 We unveil our paper accepted at the #ACL2025 Main Conference:
Automated Structured Report Generation

Let's revisit automated radiology report generation for CXR.
Free-form reports make it hard for AI systems to learn accurate generation, and even harder to evaluate. 🧵👇
@StanfordAIMI @hopprai
Reposted by Christian Bluethgen
woojinkim.com
👍 If you're interested in #LLMs in #radiology, this is a recommended read!

💯 While the article focuses primarily on LLMs, as the authors recommended, "Keep an eye on [large multimodal models]".

👉 pubs.rsna.org/doi/10.1148/...
#RadiologyAI #AIStrategy #LMMs
Reposted by Christian Bluethgen
emollick.bsky.social
When reading AI benchmarks, aside from the fact that many of the AIs are (accidentally or on purpose) trained on the test set, many tests are just bad. MMLU likely maxes out at 90% or so because so many of the questions in it are just wrong. It is also uncalibrated in difficulty across questions.
Reposted by Christian Bluethgen
tugbaakinci.bsky.social
Chaired two insightful sessions at #ECR2025 today!

"Standardization and Reporting in AI Research" with experts Hans Reitsma Mike Klontzas Annika Reinke

"How to Use ChatGPT for Academic and Administrative Tasks" Andreas S. Brendlin @cxbln.bsky.social and Ghizlane Lembarki

@myesr.bsky.social
cxbln.bsky.social
still thinking about this interaction with Claude (Oct '24) from time to time
cxbln.bsky.social
r1ing away on an ordinary machine

.. straight out of Kahnemans lesser known book "Thinking .. mostly slow"
Reposted by Christian Bluethgen
jayalammar.bsky.social
The Illustrated DeepSeek-R1

Spent the weekend reading the paper and sorting through the intuitions. Here's a visual guide and the main intuitions to understand the model and the process that created it.

newsletter.languagemodels.co/p/the-illust...
Reposted by Christian Bluethgen
gaelvaroquaux.bsky.social
People: please don't ML for the sake of ML.

I keep seeing manuscripts using fancy machine learning on brain-imaging data, where, in my opinion (having processed a lot of brain-imaging data), the method is way too complex for the richness of the data.

Fancier is not better per se
cxbln.bsky.social
my wl:
- fewer low-hanging proof-of-concept studies (yes, it works for your specialty/organ/classification, too)
- fewer head-to-head model comparisons (yes, llama 3.1 was better than llama 2 for your case, but peer review took 8 months, now there’s llama 4)

- instead: RCTs, relevant outcomes
cxbln.bsky.social
"productization requires [standardization in development and deployment], which is antithetical to research. [...] PhDs are supposed to come up with innovative ideas, validate these ideas, report the findings to the community by writing papers and then move on."

[slightly edited to fit post limits]
kyunghyuncho.bsky.social
feeling a but under the weather this week … thus an increased level of activity on social media and blog: kyunghyuncho.me/i-sensed-anx...
i sensed anxiety and frustration at NeurIPS’24 – Kyunghyun Cho
kyunghyuncho.me
cxbln.bsky.social
A market research team's dream: gathering real-world use cases and improvement opportunities directly from natural product usage

from: www.anthropic.com/research/clio
Reposted by Christian Bluethgen
maartenvsmeden.bsky.social
NEW PREPRINT

A detailed overview of 32 popular predictive performance metrics for prediction models

arxiv.org/abs/2412.10288