Evidence map›Paper›PMID 41772186›Full record

ReviewBehavior research methods2026

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

Marwa Qaraqe, Elizabeth B Varghese, Inam Qadir, Dena Al-Thani, Chahnaz T Baroudi

Abstract readReview
In one paragraph

Review in Behavior research methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Marwa QaraqeA-Sense Center of Excellence, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
Elizabeth B VargheseA-Sense Center of Excellence, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar. evarghese@hbku.edu.qa.ORCID http://orcid.org/0000-0003-4274-7092
Inam QadirA-Sense Center of Excellence, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
Dena Al-ThaniA-Sense Center of Excellence, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
Chahnaz T BaroudiRenad Academy, Qatar Foundation, Doha, Qatar.

Funding

Qatar Research Development and Innovation Council ARG01-0508-23009
6 · The paper itself

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.

Indexed as

Artificial IntelligenceAttentionAutism Spectrum DisorderAutistic DisorderChildHumansAttention assessmentAutism spectrum disorderCognitive developmentJoint attentionSocial communication

Identifiers

PMID41772186
PMCPMC12953272

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.