from Hacker News

Using Interpretability to Identify a Novel Class of Alzheimer's Biomarkers

by keeda on 2/3/26, 5:23 AM with 1 comments

• by keeda on 2/3/26, 5:23 AM

  Fascinating study where they trained an AI model on a large dataset and then used interpretability analysis to figure out what biomarkers it had "learned" to look for.

  "This is the contribution we want to highlight: interpretability as a tool for hypothesis triage. Foundation models learn from data at a scale humans can't match. If we can inspect what they've learned we can use that to guide experimental priorities, turning AI models into a source of testable hypotheses rather than a black box."