arXiv:2606. 01217v1 Announce Type: cross Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by challenges in communication and behavior.
arXiv:2609.16464v1 Announce Type: cross Abstract: The clinical management of autism spectrum disorder (ASD) faces a bottleneck in early screening, mainly because trained specialists are scarce and co...
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:2306.14300v2 Announce Type: replace-cross Abstract: Autism spectrum disorder (ASD) is a developmental condition that presents significant challenges in social interac- tion, communication, and...
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...
arXiv:2609.14159v1 Announce Type: cross Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose early diagnosis remains challenging because conventional clinical assessments...
arXiv:2602.13047v2 Announce Type: replace Abstract: Conversational speech reveals early signs of cognitive decline, including dementia and mild cognitive impairment (MCI). AI models show promise for...
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:2606. 00369v1 Announce Type: cross Abstract: Safe global deployment of AI models requires alignment with human values that vary across cultures.
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:2607. 08533v1 Announce Type: new Abstract: Understanding perceptual differences between autistic and neurotypical adults requires behavioral assays that are sensitive, reliable, and mechanistically informative.
arXiv:2607. 23977v1 Announce Type: cross Abstract: Acoustic biomarkers show promise for detecting Alzheimer's Disease (AD), yet whether the cues driving diagnostic AI align with those salient to human listeners is underexplored across languages and genders, where pathological markers and perceptual strategies differ.
The paper introduces MH‑INDIC, a framework that evaluates maternal‑health language model interactions in North Indian urban and semi‑urban settings by measuring cultural alignment across ten dimensions of reasoning. Using a survey of 102 pregnant and postpartum women, the authors assess ten LLMs, finding that while models approximate overall population‑level cultural patterns, they lack the demographic and household‑specific variation seen in humans. The study also demonstrates that human‑grounded conditioning improves both profile alignment and dialogue quality in generated interactions.