Integrating AI into clinical practice: Human-centered design requirements for next-generation sequencing workflows

Abstract

Today, the integration of next-generation sequencing (NGS) into clinical genomics is increasingly AI-driven, with artificial intelligence (AI) underpinning every stage from data processing to decision support. While NGS enables rapid and scalable analysis of complex genetic information-paving the way for precision diagnostics and stratified treatment-the transition from potential to practice is hindered by fragmented workflows, limited usability, and non-standardized data interfaces. This paper introduces a design-oriented framework for embedding AI-powered NGS workflows into clinical decision support systems (CDSS), currently focused on genetic screening and tumor testing, but with components extendable to pathogen detection scenarios. DUXU (Design, User eXperience, Usability) is presented as a conceptual and methodological framework rather than a concrete implementation; its realization is intentionally flexible and must be adapted to the requirements, constraints, and objectives of specific clinical use cases. Future work will adapt data requirements (e.g., taxonomic classification instead of variant calling), functional workflows (e.g., microbial genome assembly), and stakeholder roles (e.g., microbiologists in antimicrobial stewardship). Grounded in real-world clinical environments and aligned with standards such as FHIR and GA4GH, we highlight the central role of AI in multimodal data interpretation, patient-specific visualization, and transparent, explainable decision-making. Anchored in DUXU principles, our approach addresses the socio-technical demands of clinical genomics and proposes actionable design requirements for interoperable, role-specific interfaces. This work advances the development of explainable, interpretable, trustworthy, and operationally embedded AI-based NGS systems into clinical practice.

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