arXiv Machine Learning
A multimodal large language model for evidence-based autism spectrum disorder screening
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arXiv AI
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UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning
UniAR is a unified framework that improves autism spectrum disorder (ASD) recognition by using multi-granularity prompt learning and a large multimodal model to generate diagnostic descriptions at word, phrase, and sentence levels. It aligns these semantic representations with visual evidence through a Mixture-of-Experts-based Multi-Scale Alignment Module, enabling robust ASD detection across heterogeneous data types. Experiments on four brain MRI and facial expression benchmarks show that UniAR outperforms state‑of‑the‑art methods, achieving 75.9% accuracy on MRI and 91.6% on facial benchmarks, with gains of 1.5 and 1.2 percentage points respectively.
arXiv AI
Jul 10
Evaluating the Effect of Frame Rate in Sequence-Based Classification of Autism-Related Self-Stimulatory Hand Idiosyncrasies
arXiv:2607. 07957v1 Announce Type: new Abstract: Autism spectrum disorder (ASD) affects over 75 million individuals worldwide, yet scalable computational methods for remote behavioral screening remain limited.
arXiv AI
Aug 20
A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities
The article reviews 55 machine‑learning studies on autism spectrum disorder (ASD) published between 2017 and 2023, focusing on how ML can aid early diagnosis and treatment. It finds that supervised learning dominates current research, while deep learning is gaining traction as data volumes grow. The review highlights the need for models that fuse complex data—such as genetic, clinical, wearable, and biometric sources—to improve diagnostic accuracy and enable continuous, non‑intrusive monitoring.
arXiv Computer Vision
Oct 8
A Deterministic Evidence Layer for Vision-Language Autism Screening from Naturalistic Home Video
arXiv:2610.09217v1 Announce Type: new Abstract: Autism spectrum disorder (ASD) is diagnosed through specialist observation of a child's social behavior, and access to that expertise is the bottleneck...
arXiv AI
Oct 15
From Portraits to Pathology: Probing Diagnostic Overreach in Vision-Language Models
arXiv:2609.05540v2 Announce Type: replace-cross Abstract: Many medical conditions require clinical assessment of symptoms, history, and functioning rather than judgments from appearance. We study whe...
arXiv Computation and Language
Sep 23
SpecialEduBench: Benchmarking Vision-Language Models on Knowledge, Skill, and Attitude in Language Intervention for Autistic Children
SpecialEduBench is a new benchmark for vision‑language models that evaluates their pedagogical competence in language intervention for autistic children across knowledge, skill, and attitude dimensions. It contains 4,537 knowledge items, 200 skill items, and 68 attitude items derived from recorded interventions, with 192 response cells for attitude items that combine pressure and monitoring. Eight leading vision‑language models were tested, none achieving full performance, especially in honesty cells, indicating that current models still struggle with situated teaching tasks.