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. Their results show that repetitive behaviors, special interests, and perception-based behaviors are most associated with an autism diagnosis. These findings have potential to improve diagnostic guidelines for autism by decreasing the focus on social factors — which the established guidelines in the DSM-5 focus on but the model did not classify among the most relevant in diagnosing autism.
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