Without clear and effective biological tests for autism based on genes, brain or blood measurements, diagnosis today still largely depends on clinical assessment. The standard way of doing this is by observing how the individual fits the criteria for autism listed in gold standard manuals like the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5).
These criteria are divided into two categories: one for restricted or repetitive behaviours, actions, or activities, and another for differences in social communication and interaction. In the end, however, it is the clinician, relying on years of experience, who decides whether the individual is given an autism diagnosis. The degree to which an individuals diagnosed with autism fit the DSM-5 criteria can vary considerably.
To empirically test which criteria clinicians most often observed in people diagnosed with autism, scientists performed artificial intelligence (AI) analysis on more than 4,200 observational clinical reports from a French-speaking child cohort from Montreal, in Québec, Canada. They tailored and carried out large language modelling approaches to predict the diagnosis decision based solely on these reports. In particular, the investigators came up with a way to identify key sentences in the reports that were most relevant in a positive diagnosis, allowing direct comparison with the diagnostic criteria.
The analysis found that criteria related to socialization, such as emotional reciprocity, nonverbal communication, and developing relationships, were not highly specific to an autism diagnosis, meaning they were not found much more in individuals diagnosed with autism than those in which a diagnosis was ruled out. Criteria related to repetitive movements, highly fixated interests, and perception-based behaviors, however, were strongly linked to an autism diagnosis.
Their findings lead the scientists to argue that the medical community may want to reconsider and review the established criteria used to diagnose autism. Specifically, the heavy weighting of socialization—for several decades now—in assessing autism, which may also contribute to the increase of autism diagnosis in developed countries, may have to be reduced, with increased focus on certain repetitive behaviours and special interests. This would make diagnosis more effective and efficient, as social factors are relatively time consuming, labour intensive, and imprecise to assess compared to more obvious behavioural traits.
Receiving an autism diagnosis can take years, delaying interventions that improve outcomes. Making the assessment process more focused and streamlined may provide vast benefits to autistic people and the healthcare system.
"In the future, large language model technologies may prove instrumental in reconsidering what we call autism today," says Danilo Bzdok, a neuroscientist at The Neuro and Mila (the Quebec Artificial Intelligence Institute), and study's co-senior author.
"Such a data-driven revision of autism criteria is a complement to what has historically been done by expert panels and human judgment alone," says Laurent Mottron, a clinician-researcher at the Department of Psychiatry of Université de Montréal, and co-senior author of the study.
Their findings were published in the journal Cell on March 26, 2025. The study was funded by the Brain Canada Foundation, Health Canada, the National Institutes of Health, Canadian Institutes of Health Research, Canada First Research Excellence Fund, and the Canadian Institute for Advanced Research.
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