Artificial Intelligence-Driven Software Validation Engineering Framework for Improving Digital Healthcare Applications Supporting Individuals with Autism Spectrum Disorder
Keywords:
Software Validation Engineering, Artificial Intelligence, Autism Spectrum Disorder, Digital Health Care, Explainable AI, Accessibility, Cybersecurity, Human-in-the-Loop GovernanceAbstract
Digital health tools built for individuals with Autism Spectrum Disorder (ASD) increasingly rely on machine learning components that continue to adapt after deployment, behavioral-monitoring sensors, augmentative communication platforms, and clinician-facing decision-support dashboards, among them. Conventional software validation practice, built around a fixed pre-release test surface, was not designed to govern systems whose decision logic shifts as new data arrive. This paper proposes a validation-engineering framework tailored to AI-enabled ASD digital-health applications, treating continuous verification, explainability, accessibility, and cybersecurity as engineering disciplines rather than downstream compliance items. The framework layers a modular, human-in-the-loop validation architecture over existing medical-device software standards, translating IEC 62304 and FDA AI/ML lifecycle guidance into an operational validation pipeline suited to systems that keep learning. Drawing on the software-validation-engineering and AI-in-ASD-care literature, two bodies of work that have so far developed largely apart from one another, the paper argues that a distinct engineering discipline, not clinical or regulatory oversight alone, should anchor trust in these systems. The architectural, infrastructural, and performance layers of the proposed framework are evaluated against accessibility and equity considerations specific to neurodiverse users, and the paper closes by identifying validation-engineering research questions this framework does not yet resolve.
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