# Joint attention in autism: A narrative review of assessment techniques from behavioral observation to artificial intelligence

## TL;DR

This paper reviews how joint attention in children with autism is assessed, from traditional methods to AI-based approaches, aiming to improve early diagnosis and intervention.

## Contribution

The paper provides a comprehensive narrative review of evolving joint attention assessment techniques, emphasizing the transition to AI-driven methods.

## Key findings

- JA assessment has evolved from human-mediated to technology-assisted methods.
- AI-based approaches offer new opportunities for accurate and scalable assessment.
- Future research should focus on improving validation and accessibility of JA assessment tools.

## Abstract

Joint attention (JA), the shared focus between two individuals on an object or event, plays a pivotal role in social communication, cognitive development, and language acquisition during early childhood. However, JA is frequently impaired in children with autism spectrum disorder (ASD), highlighting the need for precise assessment to support early diagnosis and intervention. This narrative review explores the evolution of JA assessment methods, tracing the shift from human-mediated techniques to technology-driven approaches, including artificial intelligence (AI). The study analyzes research indexed in major bibliographic databases between 2002 and 2024, categorizing findings into human-mediated and technology-assisted methods. Key aspects such as target populations, data collection processes, and validation strategies are examined. By highlighting the strengths and limitations of existing approaches, the review identifies future research directions that can advance JA assessment and inform early intervention strategies, ultimately benefiting children with ASD and their families.