Objectives:The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven 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.

This systematic review includes a narrative synthesis and meta‐analysis of research on the associations between primarily non‐autistic people's characteristics and their attitudes toward autistic people. Of 47 studies included in the narrative synthesis, White undergraduate students were surveyed most frequently. Demographic characteristics were the factors most frequently tested for associations with attitudes, followed by contact‐related factors (i.e., quantity and quality), knowledge about autism, trait and personality factors, and other factors that did not fit into a single category. Internal consistency was not reported for some instruments assessing raters' characteristics; some instruments had alpha levels lower than 0.70, and many characteristics of raters were measured using one‐item measures. Moreover, theoretical motivations for investigating the raters' characteristics were rarely provided. A total of 36 studies were included in the meta‐analysis, which showed that attitudes toward autistic people were significantly associated with participants' gender, knowledge about autism, and quality and quantity of their previous contact with autistic people, but not with their age or autistic traits. These findings indicate a need for more studies that focus on context‐related characteristics (e.g., institutional variables such as support/commitment to inclusion), use reliable instruments to measure non‐autistic people's characteristics, and situate their investigation in a theoretical framework.

Objective This study aimed to examine the validity of the Korean version of the Autism Diagnostic Interview-Revised (K-ADI-R) and determine its efficacy in identifying individuals with autism spectrum disorder (ASD).Methods Data were pooled from several past and ongoing studies as well as clinical records acquired at Seoul National University Bundang Hospital from 2008 to 2017. The K-ADI-R were administered and scored by trained research reliable examiners. Measurements to investigate the validity of the K-ADI-R was through sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and Cohen’s kappa.Results A total of 1,271 (age 88.9±62.42 months, male=927) participants were included. The K-ADI-R yielded strong psychometric properties with high sensitivity (86.06–99.27%), specificity (84.75–99.55%), PPV (92.33–99.72%), and NPV (79.43–98.64%). There were significant differences in item scores across the K-ADI-R diagnostic algorithm regardless of age and sex (p<0.001). Agreement between the K-ADI-R and other ASD related measurements ranged between levels of good to excellent.Conclusion Despite language or cultural boundaries, the K-ADI-R demonstrated high levels of sensitivity, specificity, PPV, and NPV within a wide range of participants; hence, suggesting promising usage as a valuable diagnostic instrument for individuals with ASD.

Objective This study examined how state and trait anxiety of adolescents with autism spectrum disorders (ASD) are associated with their demographic characteristics, repetitive and restricted behaviors (RRBs), and internalizing and externalizing problem behaviors.Methods A total of 96 participants with ASD (mean age=14.30 years; 91 males) completed a battery of tests including the State/Trait Anxiety Inventory (STAI), the Autism Diagnostic Interview-Revised, the Social Responsiveness Scale (SRS), and a cognitive test measuring intelligence quotient (IQ). Participants’ parents completed the Child Behavior Checklist (CBCL). Pearson’s correlations among age, IQ, two subscales of the STAI (i.e., STAIS and STAIT, measuring self-reported state and trait anxiety, respectively), and the Anxiety subscale of CBCL (i.e., CBCL-Anxiety, measuring parent-reported trait anxiety) were computed. Subsequently, Pearson’s correlations were computed among the three anxiety measures, RRBs, and problem behaviors, while controlling for participants’ age and IQ.Results The STAIS and CBCL-Anxiety were both significantly correlated with higher age, sensory sensitivity, depressive symptoms, somatic complaints, and aggressive behaviors. All three anxiety variables were significantly and positively correlated with total SRS RRB scores. Additionally, the STAIS and STAIT were significantly associated with more severe Compulsion/Adherence behaviors, and the CBCL-Anxiety was also significantly associated with more severe Rule-breaking Behaviors.Conclusion Self-reported state anxiety showed association patterns similar to those of parent-reported trait anxiety. Future studies investigating the precise operationalization of different anxiety instruments are needed to accurately measure the anxiety of adolescents with ASD.

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