In diagnosing autism - the developmental variant that affects around 80 million people worldwide - medical practitioners today put too much emphasis on a child's lack of sociability and not enough on their interests and how they naturally behave spontaneously with objects.
And so, to be more accurate in their assessments, health authorities should start tapping the vast analytic powers of artificial intelligence, combined with the experience of clinicians, and come up with better diagnostic criteria.
That's what Canadian neuroscientists argue in a new study, published today in the journal Cell.
"A data-driven revision of autism criteria of the kind we're proposing, grounded on clnical certainty, would complement what has historically been done by expert panels and the judgment of humans, who can be wrong," said co-senior author Laurent Mottron, a clinician-researcher in psychiatry at Université de Montréal.
Added co-first author Emmet Rabot, an UdeM clinical associate professor of psychiatry: "This project marks the successful outcome of a fruitful partnership between McGill University and UdeM. We hope our results will make a meaningful contribution to advancing diagnosis and support for the autistic community."
The study involved Danilo Bzdok, Jack Stanley Siva Reddy and Eugene Belilovsky, all scientists at Mila - Quebec Artificial Intelligence Institute, which is affiliated with UdeM and McGill. Stanley and Bzdok are also associated with The Neuro - Montreal Neurological Institute-Hospital, which is affiliated with McGill.
DSM-5, the gold standard
With no specific markers of autism yet available in a person's genes, blood or brain, diagnosis today still largely depends on clinical assessment by physicians and their assessment teams.
The standard way of doing this is by observing how a child fits the criteria for autism listed in gold-standard manuals such as the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), as well as in standardized instruments that are mapped on DSM.
These criteria are divided into two categories: one for a child's differences in social communication and interaction and another for their restricted or repetitive behaviours, actions or activities.
In the end, however, it is the clinicians, relying on years of experience, who decide whether a child gets diagnosed - and how much they fit the DSM-5 criteria can vary a lot.
To empirically test which criteria clinicians most often observed in people diagnosed with autism, the McGill and UdeM scientists ran more than 4,200 observational clinical reports from a French-speaking cohort of children with suspected autism in Montreal through an AI program for analysis.
They tailored and carried out large language modelling (LLM) approaches to predict the diagnosis decision in each case, 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.
That then allowed them to make a direct comparison with the U.S. diagnostic criteria also accepted worldwide - with surprising results.
They found that criteria related to socialization - emotional reciprocity, nonverbal communication and developing relationships - were not highly specific to an autism diagnosis. In other words, they were not found much more in children diagnosed with autism than in those for whom a diagnosis was ruled out.
Criteria related to repetitive behaviors, highly specific interests and perception-based behaviors, however, were strongly linked to an autism diagnosis.
Reconsider and review the criteria
These findings led the scientists to argue that the medical community may want to reconsider and review the established criteria used to diagnose autism - as the current criteria seem both inadequate and responsible for the over-diagnosis of autism that has been widely documented around the world.
They should put much less weight on a child's lack of social skills, a weighting that's been emphasized for decades now, the authors argue. Challenges in socializing are common in autistic children but other atypical signs that are much easier to identify also characterize these children, they say.
Increased focus should be put on children's repetitive and perception-based behaviours and special interests, they add, as those might be more specific to autism that previously thought.
Receiving an autism diagnosis can take years, delaying interventions that improve outcomes and quality of life. Conversely, an unjustified diagnosis can lead to a whole host of bad decisions, the scientists say. Improving the assessment process, therefore, would provide vast benefits to autistic people and the public healthcare system.
"In the future, large language model technologies may prove instrumental in reconsidering what we call autism today," said Bzdok, the study's other senior author.
About this study
"Large language models deconstruct the clinical intuition behind diagnosing autism," by Jack Stanley, Emmett Rabot Eugene Belilovsky, Siva Reddy, Laurent Mottron and Danilo Bzdok, was published March 26, 2025 in Cell.
