J Korean Acad Child Adolesc Psychiatry.  2019 Oct;30(4):145-152. 10.5765/jkacap.190027.

The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review

• Song DY
• Kim SY
• Bong G
• Kim JM
• Yoo HJ

• KMID: 2464246
• DOI: http://doi.org/10.5765/jkacap.190027

Abstract

OBJECTIVES
The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective data-driven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics.
METHODS
Based on our search and exclusion criteria, we reviewed 13 studies.
RESULTS
To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data.
CONCLUSION
While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.

Keyword

Autism spectrum disorder; Artificial intelligence; Diagnosis; Screening

MeSH Terms

Artificial Intelligence*
Autism Spectrum Disorder*
Autistic Disorder*
Behavior Observation Techniques
Decision Support Systems, Clinical
Delivery of Health Care
Diagnosis*
Mass Screening*
Methods
Sensitivity and Specificity