Evidence map›Paper›PMID 42809143›Full record

ReviewEuropean journal of pediatrics2026

Pediatric social robotics in ASD: navigating AI blind-spots toward clinical validation.

Sahaj Grewal, Raymond Roy, Kiranpreet Sidhu, Kejah Bascon, Feeda Elahresh, Fateh Ahmad, Logendra Naidoo, Amedeo D'Angiulli

Abstract readReview
In one paragraph

Review in European journal of pediatrics, 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

8 authors.

Sahaj GrewalDept. of Biomedical Engineering, University of Miami, Miami, FL, USA. sxg2258@miami.edu.
Raymond RoyNeuroscience of Imagination Cognition and Emotion Research (NICER) Lab, Carleton University, Ottawa, ON, Canada.
Kiranpreet SidhuNeuroscience of Imagination Cognition and Emotion Research (NICER) Lab, Carleton University, Ottawa, ON, Canada.
Kejah BasconDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
Feeda ElahreshSchool of Medicine, Wayne State University, Detroit, MI, USA.
Fateh AhmadSchool of Medicine, Wayne State University, Detroit, MI, USA.
Logendra NaidooNeuroscience of Imagination Cognition and Emotion Research (NICER) Lab, Carleton University, Ottawa, ON, Canada.
Amedeo D'AngiulliNeuroscience of Imagination Cognition and Emotion Research (NICER) Lab, Carleton University, Ottawa, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As artificial intelligence becomes increasingly integrated into children's therapeutic environments, social robots are emerging as one of the most clinically complex AI applications for pediatric autism spectrum disorder (ASD). This mini-review examines three under-validated "blind-spot" domains: robot-wearable sensor integration, immersive VR-robot hybrid systems, and generative AI conversational interfaces. Although these technologies offer promising opportunities for adaptive and personalized intervention, current evidence remains limited by small pilot studies, insufficient longitudinal validation, and unresolved developmental safety concerns. Key risks include pediatric sensor intolerance, cognitive overload during immersive exposure, privacy concerns related to continuous data collection, and unconstrained AI-generated dialogue that may reinforce maladaptive communicative patterns in vulnerable children. We propose a five-module clinical validation roadmap focused on pediatric calibration standards, standardized developmental outcome measures, multisite randomized controlled trials, longitudinal monitoring, and human-supported safety architectures.

conclusionAdaptive social robotics offers a promising but currently unvalidated frontier in pediatric ASD care. These systems cannot yet be supported as primary therapeutic interventions, as pediatric-specific calibration standards, standardized developmental outcome measures, and adequately powered longitudinal trials remain absent. The proposed validation framework outlines a pathway for ensuring AI-supported interventions augment rather than replace authentic human-centered developmental care. WHAT IS KNOWN: • Social robots can produce measurable short-term gains in social and communication skills in children with autism spectrum disorder. • Supporting evidence derives largely from small, short-duration pilot studies conducted in controlled settings, limiting clinical applicability. WHAT IS NEW: • Three under-validated blind-spot domains are identified: robot-wearable sensor integration, VR-robot hybrid systems, and generative AI conversational interfaces. • A five-module Clinical Validation Roadmap is proposed to move these domains from pilot demonstration toward pediatric standard of care.

Indexed as

Artificial IntelligenceAutism Spectrum DisorderRoboticsChildGenerative Artificial IntelligenceHumansAutism spectrum disorderClinical validationGenerative AIPediatric therapySocial roboticsVR–robot hybridsWearable sensors

Identifiers

PMID42809143
PMCPMC13623786

What OpenQuestion holds

Textmetadata
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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.