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 synthesise the behaviours and observations that are most indicative of an autism diagnosis.

Their results show that repetitive behaviours, special interests, and perception-based behaviours are most associated with an autism diagnosis. These findings have the 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.