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.
By Lei Xin, Zeheng Wang, Jiayin Zhu, Shihong Huang, Fanhu Zeng, Changjiang Jiang, Dengbo He, Yutao Yue, Zhenglun Kong
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.
By Raunak Mondal, Peter Washington
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.
By Rafael Mu\~noz-Terol, Jes\'us Peral, Sandra Amador, David Gil
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...
By Wenqi Li, Mindi Ruan, Chuanbo Hu, Shuo Wang, Xin Li
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...
By Karan Dua, Amit Agarwal, Hitesh Laxmichand Patel, Hansa Meghwani, Jyotika Singh, Ranjeet Gupta, Graham Horwood, Tao Sheng, Avi Sil, Sujith Ravi, Dan Roth
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.
By Jihoi Na, Taeyeong Kim, Sungjune Kong, Jaemin Jung, Min Joung Park, Kyungtae Joo, Ahhyun Kim, Shim Jaechang, Sooyoung Joo, Dongjin Ka, SeJoong Kim, Jimin Kim, HyunJin Jung, Unggi Lee
arXiv:2610.31682v1 Announce Type: new
Abstract: Conversation success is important for communication, social bonds, and well-being and is often reduced in autistic-neurotypical conversations. Previous...
By Thanushi Withanage, Desi Jones, Noah Sasson, Elizabeth Redcay, Carol Espy-Wilson
AOI-Net introduces a structural face AOI-guided Eye‑Gaze Track Network that jointly models short‑term temporal dynamics and AOI‑level structural organization for Autism Spectrum Disorder detection. The network uses a gating mechanism to adaptively combine complementary representations and incorporates class‑distribution‑aware learning to address the imbalance between ASD and typically developing participants. Experiments on a large clinical eye‑tracking database with over 1,300 participants demonstrate that AOI‑Net outperforms state‑of‑the‑art methods and offers interpretable gaze‑behavior modeling for scalable AI‑driven ASD screening.
By Zhanpei Huang, Binbin Sun, Jialiang Chen, Yiou Wang, Taochen Chen, Yuzhu Ji, Yiqun Zhang, Yiu-Ming Cheung
The paper examines why speech‑based screening for Alzheimer’s disease fails to generalize across different languages, tasks, and recording protocols. Using a leave‑one‑corpus‑out evaluation on four datasets, it finds that 59 of 70 interpretable speech features show conflicting patterns between healthy controls and cognitive risk groups, with pause, silence, and speech rate being highly protocol‑sensitive. The authors propose a fusion method that combines XLM‑R text baseline scores with evidence anchors, improving mean speaker AUC to 0.785 and worst‑case AUC to 0.615, and emphasize the importance of auditing feature transferability and reporting worst‑case domain robustness.
By Zijian Lu, Sizhe Liu, Yin Zhang, Jixuan Deng, Xinrong Lin, Xinchen Yuan, Chicheng Jin, Yiping Zuo, Yuanchao Li
arXiv:2606. 01217v1 Announce Type: cross Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by challenges in communication and behavior.
By Aadithya Prabha Ramaharsha, Deevna Reddy, Uma Ranjan
arXiv:2608. 07567v1 Announce Type: cross Abstract: Functional near-infrared spectroscopy (fNIRS) is a promising modality for autism spectrum disorder (ASD) classification, yet existing approaches assume temporally aligned evaluation.
By Marios Petrov, Sahana Vinayak, Targol Bakhtiarvand, Moses Smith Guddah, Adham Atyabi, Frederick Shic, Kevin A. Pelphrey
arXiv:2607. 04010v1 Announce Type: new Abstract: Bangladesh has an estimated 1.
By Salma Hoque Talukdar Koli, Fahima Haque Talukder Jely