AI Article Synopsis

  • Language deficits are a key aspect of autism spectrum disorder (ASD), and the study focused on understanding grammatical comprehension in children with ASD.
  • The research involved 70 diagnosed children, revealing that language comprehension is the most affected area, which can negatively influence their social skills and learning.
  • Persistent issues with grammar were noted even in children with good cognitive and expressive language skills, highlighting the need for targeted rehabilitation strategies in ASD treatment.

Article Abstract

Language deficits represent one of the most relevant factors that determine the clinical phenotype of children with autism spectrum disorder (ASD). The main aim of the research was to study the grammatical comprehension of children with ASD. A sample of 70 well-diagnosed children (60 boys and 10 girls; aged 4.9-8 years) were prospectively recruited. The results showed that language comprehension is the most impaired language domain in ASD. These findings have important clinical implications, since the persistence of grammatical receptive deficits may have a negative impact on social, adaptive and learning achievements. As for the grammatical profiles, persistent difficulties were found during the school-age years in morphological and syntactic decoding in children with relatively preserved cognitive and expressive language skills. These data and the lack of a statistically significant correlation between the severity of ASD symptoms and language skills are in line with the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition) perspective that considers the socio-communication disorder as a nuclear feature of ASD and the language disorder as a specifier of the diagnosis and not as a secondary symptom anymore. The presence of receptive difficulties in school-age ASD children with relatively preserved non-verbal cognitive abilities provides important hints to establish rehabilitative treatments.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7464622PMC
http://dx.doi.org/10.3390/brainsci10080510DOI Listing

Publication Analysis

Top Keywords

grammatical comprehension
8
children autism
8
autism spectrum
8
spectrum disorder
8
difficulties school-age
8
children preserved
8
language skills
8
children
6
language
6
asd
6

Similar Publications

With breakthroughs in Natural Language Processing and Artificial Intelligence (AI), the usage of Large Language Models (LLMs) in academic research has increased tremendously. Models such as Generative Pre-trained Transformer (GPT) are used by researchers in literature review, abstract screening, and manuscript drafting. However, these models also present the attendant challenge of providing ethically questionable scientific information.

View Article and Find Full Text PDF

Understanding how children acquire syntactic structures from a limited set of grammatical rules and use them creatively to convey meaning has been a longstanding interest for scientific communities. Previous studies on syntactic development have revealed its close correlation with the development of vocabulary and working memory. Our study sought to elucidate how the relations between syntactic processing, word processing, and working memory were instantiated in the brain, and how earlier neural patterns might predict language abilities one year later.

View Article and Find Full Text PDF

Performance of ChatGPT in French language analysis of multimodal retinal cases.

J Fr Ophtalmol

December 2024

Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada; Department of Ophthalmology, St. Michael's Hospital/Unity Health Toronto, Toronto, Ontario, Canada. Electronic address:

Purpose: Prior literature has suggested a reduced performance of large language models (LLMs) in non-English analyses, including Arabic and French. However, there are no current studies testing the multimodal performance of ChatGPT in French ophthalmology cases, and comparing this to the results observed in the English literature. We compared the performance of ChatGPT-4 in French and English on open-ended prompts using multimodal input data from retinal cases.

View Article and Find Full Text PDF

Lumbar spinal stenosis (LSS) is a major cause of chronic lower back and leg pain, and is traditionally diagnosed through labor-intensive analysis of magnetic resonance imaging (MRI) scans by radiologists. This study aims to streamline the diagnostic process by developing an automated radiology report generation (ARRG) system using a vision-language (VL) model. We utilized a Generative Image-to-Text (GIT) model, originally designed for visual question answering (VQA) and image captioning.

View Article and Find Full Text PDF
Article Synopsis
  • Mild cognitive impairment and early-stage dementia are often underdiagnosed, leading to increased healthcare costs; this study aims to address this gap by using patient-nurse conversations recorded in home healthcare settings to develop an AI tool for early detection of cognitive decline.
  • The research involved analyzing audio from conversations of 47 patients, identifying key linguistic features related to cognitive decline, and comparing the effectiveness of a speech processing algorithm to traditional electronic health record data.
  • Results showed that the combined approach of using verbal communication and EHR data significantly outperformed either method alone, highlighting that certain speech patterns can predict cognitive decline, thus potentially improving patient care and reducing emergency healthcare visits.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!