A Standards-Mapped Accessibility-by-Design Framework for AI-Driven Smart Healthcare Solutions: Application to the NeuroPredict Platform

Vollständiger Abstract

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AI-driven smart healthcare platforms increasingly combine multimodal data acquisition, remote monitoring, predictive analytics, longitudinal dashboards, and role-specific information services. While these capabilities enable more continuous and personalized health monitoring, they also introduce accessibility challenges that extend beyond conventional user-interface design, particularly in how monitoring results and AI-generated outputs are structured, contextualized, and communicated. This paper addresses the gap in operationalizing accessibility at the system level by proposing a standards-mapped Accessibility-by-Design framework for AI-enabled smart healthcare platforms. Requirements derived from international accessibility, human-centred design, and software quality standards are translated into traceable architectural and component-level constraints and integrated into the development workflow. The methodology combines standards-to-requirement-to-component traceability mapping, architectural documentation analysis, component inspection, and controlled workflow walkthroughs. The framework is applied to the NeuroPredict platform, an AI-based environment for longitudinal and multimodal monitoring of neurodegenerative disorders. Accessibility is operationalized through cross-layer architectural mechanisms, including semantic interface components, predictable interaction workflows, accessible presentation mechanisms, and structured representation of monitoring and AI outputs. AI-generated results are presented in a structured form together with contextual, temporal, source-related, and explanatory information to support role adaptation; uncertainty-related fields are reserved for future validated user-facing integration. Implementation evidence from selected platform workflows demonstrates that the proposed mechanisms can be incorporated into the current platform architecture and provides preliminary support for their technical feasibility. The evidence remains limited to implemented and inspected mechanisms, without user-based accessibility validation, formal conformance auditing, clinical deployment, or regulatory certification. These results illustrate that, while maintaining the distinction between accessibility mechanisms and the underlying analytical computation, accessibility-by-design can offer an engineering foundation for integrating accessibility requirements into the architecture and development lifecycle of AI-driven smart healthcare platforms.