Repetitive behaviors, special interests are indicative of autism diagnosis rather than social skills: Study
Repetitive behaviors, special interests are indicative of autism diagnosis rather than social skills: Study
Washington DC [US], March 27 (ANI): People with autism are typically diagnosed by clinical observation and assessment. To deconstruct the clinical decision process, which is often subjective and difficult to describe, researchers used a large language model (LLM) to synthesize the behaviors and observations that are most indicative of an autism diagnosis.
Our goal was not to suggest that we could replace clinicians with AI tools for diagnosis,” says senior author Danilo Bzdok of the Mila Quebec Artificial Intelligence Institute and McGill University in Montreal.
“Rather, we sought to quantitatively define exactly what aspects of observed behavior or patient history a clinician uses to reach a final diagnostic determination. In doing so, we hope to empower clinicians to work with diagnostic instruments that are more in line with their empirical realities.”
The scientists leveraged a transformer language model, which was pre-trained on about 489 million unique sentences. They then fine-tuned the LLM to predict the diagnostic outcome from a collection of more than 4,000 reports written by clinicians working with patients considered for autism diagnosis.
The reports, which were often used by multiple clinicians, included accounts of observed behavior and relevant patient history but did not include a suggested diagnostic outcome.
The team developed a bespoke LLM module that pinpointed specific sentences in the reports that were most relevant to a correct diagnosis prediction.
They then extracted the numerical representation of these highly autism-relevant sentences and compared them directly with the established diagnostic criteria enumerated in the DSM-5.
