Work

Comparison of Human Experts and AI in Predicting Autism from Facial Behavior.

Year: 2023

Type: conference-paper

Abstract: = 42 participants in a naturalistic conversation. Results show that the AI algorithm achieves an average accuracy of 80.5%, which is comparable to that of clinicians with expertise in autism (83.1%) and clinical research staff without specialized expertise (78.3%). Critically, diagnoses that were inaccurately predicted by most humans (experts and non-experts, alike) were typically correctly predicted by AI. Our results highlight the potential of AI as an assistive tool that can augment clinician diagnostic decision-making.

Source: PubMed

Authors: Evangelos Sariyanidi, Casey J Zampella, Ellis DeJardin, John D Herrington, Robert T Schultz, Birkan Tunc

Institutions: Children's Hospital of Philadelphia, University of Pennsylvania

Language: English

Topic: Autism Spectrum Disorder Research

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Mental Health, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Awards: Predicting Autism and Social Functioning from Computer Vision Analyses of Motor Synchrony During Dyadic Interactions, A computer vision toolbox for computational analysis of nonverbal social communication, Novel computer vision-based assessment of infant-caregiver synchrony as an early level II screening tool for autism, The Intellectual and Developmental Disabilities Research Center (IDDRC) at CHOP/Penn

OpenAlex ID: W4389244588

FWCI: 2.558

Cites: 21

Cited by: 5

Related to: 10