Autism Technology
Zahorodny , and most of them are Black or Latino . Meanwhile , despite mounting evidence that autism is more prevalent in girls than once believed , boys are still
four times more likely to receive a diagnosis than girls , and boys are also
diagnosed earlier than girls . Across
race and ethnicity ,
socioeconomic status , geography , and gender , the story is the same : children from less advantaged and minority groups receive a diagnosis and begin individualized treatment later than Caucasian , male , and wealthier children .
Despite these realities , I am optimistic that the healthcare community is increasingly aware of the existing disparities and is ready to embrace technology designed to tackle these challenges headon , so that we can improve the lives of children and families living with autism .
As Boston University-based pediatrician Sarabeth Broder-Fingert
points out , most autism research has focused on “ white , higher-income children and families ”, and primarily young white males . This means that when children , particularly non-white males , are evaluated for autism , assessments are largely based on data that isn ’ t always representative of all children . This has real-world consequences for non-white children and their families .
Aggravating the challenges are disparities in access to healthcare resources , differing levels of education and broad understanding of autism , language barriers , and more . Figuring out the complex healthcare system is difficult for families , especially those with limited resources .
With the help of AIbased diagnostics , physicians can have the opportunity to make earlier and more accurate diagnoses .
In an ideal world , pediatricians — who are generally the doctors that children and families see most often — would have more tools and resources to respond to concerns for autism and / or developmental progress of children , in order to coordinate appropriate care in a timely , effective manner .
AI technology help make this a reality ?
There ’ s been plenty of talk about the potential of artificial intelligence ( AI ) in a number of industries . At its core , AI is a way to understand and make sense of a vast amount of information .
In this case , that includes different types of data — video of children at play , answers to questions from parents and healthcare providers , clinical data of children with and without autism — that reflect the many characteristics of the autism spectrum , such as eye contact and a child ’ s response to social cues and emotional exchanges .
AI does more than simply crunch data , however . Its real value lies in its ability to pinpoint subtle relationships between different data points . By simultaneously analyzing hundreds or even thousands of data points , AI algorithms can identify and predict behavioral patterns that point towards or away from autism .
This helps physicians to make more informed and efficient assessments , in contrast to a scenario