Innovative and integrative approaches to ASD diagnostics: from genotype and endophenotype to artificial intelligence–based digital biomarkers

This article presents a review of contemporary research on the diagnosis of autism spectrum disorder (ASD). It discusses the current state of knowledge regarding the complex etiology of ASD, which involves genetic, epigenetic, environmental, and neurobiological factors. Despite significant scientific progress, no single biomarker has yet been identified that would allow for an unequivocal diagnosis, which increases the importance of both biological and digital biomarkers such as eye‑tracking analysis, virtual reality environments, speech analysis, and tools based on artificial intelligence and machine learning. The review also includes findings from neurophysiological and neuroimaging studies (EEG, QEEG, MRI, fMRI), which indicate abnormalities in functional connectivity, disruptions in brain oscillatory activity, and difficulties in processing social stimuli. These results suggest that modern technologies enable increasingly objective, multidimensional, and early assessment of individuals with ASD, supporting the timely implementation of appropriate therapeutic interventions. The final part of the article presents the possibility of integrating selected psychological and neurobiological models to better understand the individual profile of a person with ASD. Briefly discussed are Pąchalska’s (2019) microgenetic model of symptom formation, emphasizing the multilayered structure of the self system, and Gazzaniga’s (2013) concept of the “interpreter,” which describes how the brain constructs narratives explaining one’s own behavior. Integrating these perspectives indicates that individuals with ASD differ not only in neurobiological mechanisms but also in how they interpret social cues and construct self‑narratives. Both approaches support the development of personalized, multimodal diagnostic strategies that take into account biological variability and individual cognitive style, potentially leading to more precise and contextually appropriate interventions.