Rethinking autism diagnosis with AI and clinical expertise
It is vital to consider a child's overall behavior and interests when diagnosing autism, rather than solely focusing on sociability. By looking at how a child interacts with objects and their natural behaviors, a more comprehensive understanding of their condition can be achieved. This approach may lead to more accurate and personalized diagnoses, ultimately benefiting individuals with autism and their families. Emphasizing a holistic view of a child's behavior can also help identify their strengths and potential areas for growth. As research continues to evolve, a more nuanced understanding of autism diagnosis and treatment can be developed.
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.
AI and Clinical Expertise for Better Diagnosis
A data-driven revision of autism criteria of the kind we're proposing, grounded on clinical certainty, would complement what has historically been done by expert panels and the judgment of humans, who can be wrong," Laurent Mottron, co-senior author, clinician-researcher in psychiatry at Université de Montréal.
Added co-first author Emmet Rabot, a 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.
Current Diagnostic Practices
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 behaviors, actions, or activities.
Revising Diagnostic 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 behaviors and special interests, they add, as those might be more specific to autism than previously thought.
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.