Toward FIAP^®-Digital: an interpretable multimodal AI architecture for translational stratification and precision care...

Autism spectrum disorder is characterized by substantial heterogeneity across biological burden, adaptive reserve, developmental timing, therapeutic engagement, and intervention responsiveness. This heterogeneity increasingly motivates the development of multimodal and artificial intelligence-supported approaches in autism research. However, many existing approaches emphasize diagnostic classification, symptom prediction, or generic outcome modeling, while fewer frameworks explicitly preserve clinically interpretable constructs linking biological burden, developmental state, therapeutic accessibility, and responsiveness to intervention. This Hypothesis and Theory article proposes FIAP^®-Digital as a conceptual, hypothesis-generating, and human-supervised multimodal AI architecture for future translational stratification research in autism. FIAP^®-Digital is designed to integrate multimodal inputs-including biological, physiological, developmental, contextual, therapeutic-process, and longitudinal indicators-into structured estimates of FIAP^® constructs such as the Biological Burden Index, energetic capacity, adaptive neurodevelopmental window accessibility, therapeutic engagement accessibility, neuroplastic accessibility, and higher-order translational profiles. The proposed architecture is construct-preserving rather than purely predictive. It emphasizes multimodal data organization, construct-level representation, temporal updating, uncertainty visibility, explainability, clinician-in-the-loop interpretation, and Responsible AI governance. The manuscript outlines candidate computational components, including multimodal fusion, temporal modeling, missing-data handling, uncertainty estimation, explainability mechanisms, and clinician-readable outputs. It also proposes operational computational representations of FIAP^® constructs and a stepwise human-supervised profiling workflow. Importantly, FIAP^®-Digital is not presented as a validated diagnostic tool, treatment recommendation system, autonomous AI model, or clinically proven platform. The constructs described remain hypothetical and require empirical validation. The article therefore provides a neuroinformatics architecture and validation roadmap, including feasibility testing, construct validity, internal and external validation, longitudinal robustness, clinician usability, fairness assessment, and uncertainty calibration. Its primary contribution is to define a responsible computational pathway for testing whether FIAP^® constructs can support future precision-stratified autism research.