Data Sheet 2_An in-silico comparative cross-sectional diagnostic- accuracy study evaluating artificial intelligence platforms in identifying autism spectrum disorder from standardized pediatric case vignettes 2026.pdf

Background and objective<p>This study evaluates the capability of various general-purpose and healthcare-specialized Artificial Intelligence (AI) platforms in identifying Autism Spectrum Disorder (ASD) from clinical narratives.</p>Methods<p>Using twenty standardized pediatric case reports (10 ASD and 10 non-ASD), the evaluation assessed diagnostic accuracy, concordance with clinical diagnoses, and statistical performance across different AI architectures.</p>Results<p>The platforms demonstrated diverse operational profiles; Gemini 3 Pro achieved the highest rates of sensitivity and specificity, while other evaluated models exhibited sensitivity rates ranging from 60% to 90%.

While statistical differences in performance between general-purpose and specialized systems were not significant (P> 0.799), advanced large language models showed the ability to reason through complex diagnostic narratives.</p>Conclusion<p>These findings suggest that advanced general-purpose AI platforms can offer valuable support in interpreting complex ASD case narratives. To ensure clinical safety, incorporating stratified frameworks and standardized protocols remains essential.