Aug 2026· Theoretical and Natural Science· Vol 187, pp. 152-156· 0 citations
TL;DR
It is argued that equitable implementation of AI-assisted autism diagnosis requires culturally representative datasets, participatory model development, and greater local ownership of digital health technologies to ensure that AI promotes both diagnostic accessibility and global health equity.
Abstract
Artificial intelligence (AI) has rapidly emerged as a promising tool for improving autism spectrum disorder (ASD) screening, particularly in regions where access to trained specialists remains limited. Recent advances in machine learning have demonstrated encouraging diagnostic performance through the analysis of facial expressions, speech, eye gaze, and other behavioral markers, offering the potential to expand early diagnosis and reduce disparities in healthcare access. However, existing research has focused predominantly on algorithm development and diagnostic accuracy, while comparatively little attention has been paid to the broader ethical, cultural, and geopolitical implications of AI-assisted autism diagnosis. Drawing upon perspectives from philosophy of medicine and science and technology studies, this paper examines how AI redistributes power through three interconnected shifts: the transfer of diagnostic authority from clinicians to algorithms, the globalization of culturally specific definitions of "normal" behavior through Western-trained datasets, and the emergence of technological dependency on foreign-controlled AI infrastructure. Rather than rejecting AI-assisted diagnosis, this paper argues that equitable implementation requires culturally representative datasets, participatory model development, and greater local ownership of digital health technologies to ensure that AI promotes both diagnostic accessibility and global health equity.
A comprehensive review of recent advancements in ASD research, with particular emphasis on neuroimaging, artificial intelligence (AI), and machine learning (ML)-based diagnostic approaches, highlights the growing potential of AI-driven tools for supporting early ASD diagnosis and emphasizes the need for standardized protocols, external validation, explainable AI, and clinically translatable frameworks.
Kuljeet Singh, Khushi Mogha, S. Moctar· Neurological Sciences· 0 citations
It is argued that AI’s most important contribution to autism care is unlikely to lie in marginal improvements in classification accuracy alone, and its potential value lies in expanding access, supporting task-sharing, shortening diagnostic pathways, enabling population-oriented screening, and reaching under-recognised groups.
Xugao Han, Lingyan Weng, Houxi Xu· Frontiers in Public Health· 0 citations
It is argued that meaningful clinical impact will require the integration of multimodal learning, semantic knowledge representation, explainable reasoning, and human-in-the-loop decision processes to support safe, interpretable, and clinically deployable AI systems in pediatric healthcare environments.
R. Zgheib, A. Naggar, Arash Kermani Kolankeh et al.· Information· 0 citations
Autism spectrum disorder (ASD) is a developmental disorder that must be identified early but traditional diagnosis can be examiner-dependent, time-consuming and not accessible. This study included a dual bibliometric and systematic review on the evidence supporting the application of artificial intelligence (AI) tools in the detection of ASD according to PRISMA 2020 and SWiM guideline. A total of 41 empirical studies were identified after a search in the Scopus database. The studies were conducted between 2011 and 2026, in 11 data modalities, 7 algorithm families and 28 countries. The accuracy of the reports was between 68.18% and 100% with a median of 91%. Facial image research was the biggest modality, but all nine studies used the same public Kaggle dataset. Unstated validation designs occurred in 46% of studies, and 93% were at high risk of bias in the areas of patient selection and reference standards, and unclear flow and timing. Very high accuracy scores were massed in small, one cohort datasets, with population level studies obtaining significantly lower accuracy scores. But in general, there was only a small difference in median accuracy between the externally validated studies and the single-cohort studies (1.5 percentage points). The results suggest that the use of AI to detect ASD is not yet ready for clinical use. Transparent reporting of validation, independent external cohorts, diverse datasets, and prospective testing of positive predictive value in realistic population prevalence should be the focus of future research.
Shivani Pant, Anita Gehlot, Neha Singh et al.· International Journal for Gl...· 0 citations
Artificial intelligence shows promise as a supportive tool for early screening, but current evidence supports its use as a complement to, rather than replacement for, clinical assessment.
Andrea Catalina Mahecha Ballesteros, Juanita Valeria García Bello, Eleaine Scarlet González Zuñiga et al.· Current Psychiatry Reports· 0 citations
Autism Spectrum Disorder (ASD) is a neurological and developmental condition characterized by challenges in social interaction, communication (both verbal and non-verbal), and repetitive behaviours. While genetics play a key role in its onset, early diagnosis remains essential for effective intervention. Machine learning (ML) offers a promising approach to streamline and accelerate ASD detection, making it faster and more cost-effective than traditional methods. This paper evaluates eight classification models to identify key ASD features and automate diagnosis. We compare their performance on large datasets to enhance predictive accuracy. ML has transformed healthcare by leveraging vast data volumes for analysis, with technological advances over the past decade improving diagnostic tools now standard in medical settings. ASD affects individuals variably, with symptoms typically appearing between 18 months and 3 years. Although genetic and environmental factors contribute, no single cause is confirmed. Traditional screenings rely heavily on clinician expertise, involving manual assessments and scoring, which can be subjective and time-consuming—even experts face uncertainties in predicting onset or severity. Parents seek rapid, reliable results. ML and deep learning (DL) address these gaps by analyzing complex patterns in data, enabling early prediction of ASD and its severity. This study implements diverse algorithms to support precise, automated screening, reducing diagnostic delays and improving outcomes.
Devireddy Mamatha, K. Maheswari· 2026 6th International Confe...· 0 citations