ReviewNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2026
AI and neuroimaging in autism spectrum disorder: advances in diagnosis, methodological challenges and future directions.
Review in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAutism Spectrum Disorder (ASD) is characterized by challenges in social interaction, communication, and restricted or repetitive behaviors, with motor abnormalities increasingly recognized as potential indicators for early identification.
objectiveThis paper presents a comprehensive review of recent advancements in ASD research, with particular emphasis on neuroimaging, artificial intelligence (AI), and machine learning (ML)-based diagnostic approaches. The review examines global and country-specific prevalence trends, current diagnostic methodologies, and existing therapeutic interventions.
methodsThrough a critical analysis of the literature, including experimental and review studies, key challenges are identified, such as small sample sizes, limited population diversity, heterogeneous imaging protocols, restricted generalizability, and reliance on single-modal datasets. The review further summarizes publicly available ASD datasets and evaluates the strengths and limitations of contemporary AI-driven neuroimaging approaches for ASD diagnosis.
resultsThe contributions of this study include a comprehensive synthesis of neuroimaging and AI-based diagnostic methods, an analysis of available datasets, and a critical evaluation of current methodological challenges and future research directions.
conclusionThe findings highlight the growing potential of AI-driven tools for supporting early ASD diagnosis while emphasizing the need for standardized protocols, external validation, explainable AI, and clinically translatable frameworks. Future research should focus on improving dataset diversity, conducting multicenter clinical validation studies, and integrating adaptive learning methodologies to enhance the reliability and applicability of ASD diagnostic systems. This study contributes to the growing body of multidisciplinary research aimed at advancing early diagnosis, personalized intervention, and evidence-based clinical decision support for individuals with ASD.
Indexed as
Identifiers
42625082What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.