Facial data for autism spectrum disorder detection using machine learning, deep learning, and emerging large language...

Autism spectrum disorder (ASD) is a neurodevelopmental condition whose timely identification can support earlier intervention. Conventional assessment relies on specialist observation and caregiver reports, motivating research into computer-aided screening using facial and behavioral data. This systematic review examines 171 studies published between 2016 and May 2026. It synthesizes machine learning and deep learning approaches to ASD assessment using static facial images, facial video, eye tracking, and multimodal data containing a facial signal. It also covers large language models, vision-language models, and multimodal large language models applied to ASD assessment, which were not addressed by the previous reviews included in the structured comparison, together with facial-data foundation-model studies in other clinical conditions that offer transferable methods. The review compares datasets, modeling strategies, and evaluation practices, and assesses methodological quality and risk of bias. Reported performance was often promising, but the strength of evidence varied with dataset provenance, diagnostic confirmation, participant independence, and validation design. Static-image studies relied heavily on reused public datasets and image-level partitions, whereas facial-video, eye-tracking, and multimodal studies more often used participant-level cohorts but still provided limited external and prospective validation. Differences in populations, acquisition protocols, prediction targets, and evaluation units prevent direct ranking of reported metrics across studies. Foundation models broaden the field toward behavioral description, diagnostic reasoning, and clinician-readable reporting, although their evidence remains preliminary. Facial-related data therefore show potential for computer-aided ASD screening, but clinical translation depends on clinically verified datasets, participant-independent evaluation, and testing in independent cohorts.