Cancer diagnosis makes for an interesting RL environment for LLMs
Hey HN, this is David from Aluna (YC S24). We work with diagnostic labs to build datasets and evals for oncology tasks. I wanted to share a simple RL environment I built that gave frontier LLMs a set of tools that lets it zoom and pan across a digitized pathology slide to find the relevant regions to make a diagnosis. Here are some videos of the LLM performing diagnosis on a few slides: (https://www.youtube.com/watch?v=k7ixTWswT5c): traces of an LLM choosing different regions to view before making a diagnosis on a case of small-cell carcinoma of the lung…
Cancer diagnosis makes for an interesting RL environment for LLMs launched on November 12, 2025 with 46 votes, #470 of 9,795 launches that month and more than 90% of that year's launches.
In plain words
Aluna has created a reinforcement learning environment that enables large language models to analyze digitized pathology slides for cancer diagnosis. The system allows LLMs to zoom and pan across slides to locate relevant regions before making diagnostic determinations. Built for diagnostic labs conducting oncology work, the tool provides a framework for developing datasets and evaluation methods for pathology-based AI tasks. The environment demonstrates how frontier language models can systematically examine medical imagery to support cancer identification.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN, this is David from Aluna (YC S24). We work with diagnostic labs to build datasets and evals for oncology tasks. I wanted to share a simple RL environment I built that gave frontier LLMs a set of tools that lets it zoom and pan across a digitized pathology slide to find the relevant regions to make a…
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