by McGill University
Credit: Unsplash/CC0 Public Domain
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 meets 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 behaviors, 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 individual diagnosed with autism fits 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 modeling approaches to predict the diagnosis decision based solely on these reports.
Their findings were published in the journal Cell.
In particular, the investigators came up with a way to identify key sentences in the reports that were most relevant to 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 in autism diagnosis in developed countries, may have to be reduced, with increased focus on certain repetitive behaviors and special interests. This would make diagnosis more effective and efficient, as social factors are relatively time-consuming, labor-intensive, and imprecise to assess compared to more obvious behavioral 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 health care 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 the 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.
More information: Language models deconstruct the clinical intuition behind diagnosing autism, Cell (2025). DOI: 10.1016/j.cell.2025.02.025. www.cell.com/cell/fulltext/S0092-8674(25)00213-2
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