{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T06:16:49Z","timestamp":1788243409535,"version":"build-2803163510"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:p>\n                    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\n                    <jats:sup>\u00ae<\/jats:sup>\n                    -Digital as a conceptual, hypothesis-generating, and human-supervised multimodal AI architecture for future translational stratification research in autism. FIAP\n                    <jats:sup>\u00ae<\/jats:sup>\n                    -Digital is designed to integrate multimodal inputs-including biological, physiological, developmental, contextual, therapeutic-process, and longitudinal indicators-into structured estimates of FIAP\n                    <jats:sup>\u00ae<\/jats:sup>\n                    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\n                    <jats:sup>\u00ae<\/jats:sup>\n                    constructs and a stepwise human-supervised profiling workflow. Importantly, FIAP\n                    <jats:sup>\u00ae<\/jats:sup>\n                    -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\n                    <jats:sup>\u00ae<\/jats:sup>\n                    constructs can support future precision-stratified autism research.\n                  <\/jats:p>","DOI":"10.3389\/fninf.2026.1868068","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T06:06:27Z","timestamp":1788242787000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward FIAP\u00ae-Digital: an interpretable multimodal AI architecture for translational stratification and precision care in Autism"],"prefix":"10.3389","volume":"20","author":[{"given":"Yves","family":"Fuamba","sequence":"first","affiliation":[{"name":"Department of Autism Research, Antibiostress Clinics","place":["Gloucester, ON, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.inffus.2023.03.008","article-title":"A systematic review of trustworthy and explainable artificial intelligence in healthcare: assessment of quality, bias risk, and data fusion","volume":"96","author":"Albahri","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1186\/s12911-020-01332-6","article-title":"Explainability for artificial intelligence in healthcare: a multidisciplinary perspective","volume":"20","author":"Amann","year":"2020","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref3","volume-title":"Diagnostic and Statistical Manual of Mental Disorders: DSM-5-TR","year":"2022"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","article-title":"Multimodal machine learning: a survey and taxonomy","volume":"41","author":"Baltru\u0161aitis","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1079006","DOI":"10.3389\/fpsyt.2022.1079006","article-title":"Precision medicine approaches for heterogeneous conditions such as autism spectrum disorder","volume":"13","author":"Beversdorf","year":"2023","journal-title":"Front. Psych."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"827406","DOI":"10.3389\/fpsyt.2022.827406","article-title":"Rethinking autism intervention science: a dynamic perspective","volume":"13","author":"Chen","year":"2022","journal-title":"Front. Psych."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1529839","DOI":"10.3389\/fninf.2024.1529839","article-title":"Editorial: improving autism spectrum disorder diagnosis using machine learning and neuroinformatics","volume":"18","author":"Elbattah","year":"2024","journal-title":"Front. Neuroinform."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"106520","DOI":"10.1016\/j.nbd.2024.106520","article-title":"Biomarkers of mitochondrial dysfunction in autism spectrum disorder: a systematic review and meta-analysis","volume":"197","author":"Frye","year":"2024","journal-title":"Neurobiol. Dis."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"eaay7120","DOI":"10.1126\/scirobotics.aay7120","article-title":"XAI\u2014Explainable artificial intelligence","volume":"4","author":"Gunning","year":"2019","journal-title":"Sci. Robot."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1111\/jcpp.13176","article-title":"Annual research review: looking back to look forward-changes in the concept of autism and implications for future research","volume":"61","author":"Happ\u00e9","year":"2020","journal-title":"J. Child Psychol. Psychiatry"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1514678","DOI":"10.3389\/fnins.2024.1514678","article-title":"Mapping the structure of biomarkers in autism spectrum disorder: a review of the most influential studies","volume":"18","author":"Jin","year":"2024","journal-title":"Front. Neurosci."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","article-title":"Key challenges for delivering clinical impact with artificial intelligence","volume":"17","author":"Kelly","year":"2019","journal-title":"BMC Med."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1016\/S0140-6736(13)61539-1","article-title":"Autism","volume":"383","author":"Lai","year":"2014","journal-title":"Lancet"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1038\/s41380-018-0321-0","article-title":"Big data approaches to decomposing heterogeneity across the autism spectrum","volume":"24","author":"Lombardo","year":"2019","journal-title":"Mol. Psychiatry"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1038\/s41572-019-0138-4","article-title":"Autism spectrum disorder","volume":"6","author":"Lord","year":"2020","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"1085445","DOI":"10.3389\/fpsyt.2023.1085445","article-title":"Does the current state of biomarker discovery in autism allow for the implementation of precision medicine?","volume":"14","author":"Loth","year":"2023","journal-title":"Front. Psych."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"985713","DOI":"10.3389\/fpsyt.2022.985713","article-title":"Mitochondrial allostatic load as a mediator between autism and psychopathology","volume":"13","author":"Mahony","year":"2022","journal-title":"Front. Psych."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.3390\/diagnostics15172232","article-title":"An explainable deep learning framework for multimodal autism diagnosis using XAI GAMI-net and hypernetworks","volume":"15","author":"Malik","year":"2025","journal-title":"Diagnostics"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1017\/ipm.2021.73","article-title":"Can stratification biomarkers address the heterogeneity of autism spectrum disorder?","volume":"39","author":"Molloy","year":"2022","journal-title":"Ir. J. Psychol. Med."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"109370","DOI":"10.1016\/j.compeleceng.2024.109370","article-title":"A review of explainable artificial intelligence in healthcare","volume":"118","author":"Sadeghi","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1109\/JBHI.2017.2767063","article-title":"Deep EHR: a survey of recent advances in deep learning techniques for electronic health record analysis","volume":"22","author":"Shickel","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"1628216","DOI":"10.3389\/fpsyt.2025.1628216","article-title":"Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review","volume":"16","author":"Sohn","year":"2025","journal-title":"Front. Psych."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."}],"container-title":["Frontiers in Neuroinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2026.1868068\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T06:06:28Z","timestamp":1788242788000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2026.1868068\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,1]]},"references-count":25,"alternative-id":["10.3389\/fninf.2026.1868068"],"URL":"https:\/\/doi.org\/10.3389\/fninf.2026.1868068","relation":{},"ISSN":["1662-5196"],"issn-type":[{"value":"1662-5196","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,1]]},"article-number":"1868068"}}