Fine-tuning a multimodal large language model for clinician-grade autism behavioral scoring from short home videos
Researchers fine-tuned Gemini 2.5 Pro on 400 clinician-rated home videos using low-rank adaptation, achieving significant improvements in inter-rater reliability and ASD diagnosis accuracy, matching or exceeding clinician performance. The approach enables scalable behavioral feature extraction for autism assessment.
[2606.27484] Fine-tuning a multimodal large language model for clinician-grade autism behavioral scoring from short home videos
[Submitted on 25 Jun 2026]
Title:Fine-tuning a multimodal large language model for clinician-grade autism behavioral scoring from short home videos
View a PDF of the paper titled Fine-tuning a multimodal large language model for clinician-grade autism behavioral scoring from short home videos, by Mohammadmahdi Honarmand and 11 other authors
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Abstract:Autism spectrum disorder (ASD) affects 1 in 31 US children, yet median age at diagnosis exceeds four years. Artificial intelligence pipelines that provide quantified diagnosis using easy to access observational data (e.g., home videos) could help with earlier diagnosis, and timely delivery of early treatments. We fine-tuned Gemini 2.5 Pro on 400 clinician-rated home videos with low-rank adaptation, training only on 30 behavioral features previously validated to produce reliable predictions when passed to various ML models. On 99 held-out children (49 ASD, 50 neurotypical), inter-rater reliability with clinicians (per-feature weighted Cohen's kappa) improved by 40% (p
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