💨 Abstract

Researchers used a language model to analyze behaviors and observations associated with an autism diagnosis, finding that repetitive behaviors, special interests, and perception-based behaviors were most indicative. The findings suggest a potential improvement in diagnostic guidelines by decreasing the focus on social factors, as the model did not classify them among the most relevant. The goal was not to replace clinicians with AI tools but to quantify what aspects of behavior or patient history clinicians use for a final diagnostic determination.

Courtesy: theprint.in

Summarized by Einstein Beta 🤖