Interpretable Symptom Vectors for Depression in a Large Language Model
Finding where depression symptoms hide inside AI language models
Researchers discovered that AI language models like Gemma internally represent the different symptoms of depression in a way that matches how clinicians assess them. By examining the model's internal computations, they found a specific layer where mood problems, physical symptoms, and suicidality thoughts separate into distinct patterns, and they could read these patterns directly from the model's thinking process — even on new patient descriptions the model had never seen before.
Depression today is usually reduced to a single score, ignoring the fact that patients suffer different symptom mixes. If AI tools can be designed to measure individual symptoms accurately and in ways doctors understand, they could help clinicians spot which patients need which treatments. This work shows it's possible to build depression-screening tools that are both powerful and transparent about how they reach their conclusions, potentially making AI more trustworthy in mental health care.