By Anil Mogha, Ashwin Kumar Nyalakonda, and Gourav Garg — K3 Innovations, Inc. (Paper ML08).
Abstract
Purely metadata-driven automation in clinical trials provides strong regulatory alignment and deterministic outputs but struggles to adapt to evolving protocols and study-specific nuances. Conversely, general-purpose Large Language Models (LLMs) offer flexibility and rapid development but lack native CDISC awareness, traceability, and the compliance required for regulatory submissions. This paper presents SPARC AI, a hybrid, metadata-native clinical trial automation platform that combines deterministic metadata-based engines with LLM-augmented intelligence.
SPARC operates directly on CDISC standards such as SDTM, ADaM, and Define.xml, enabling automated ADaM specification creation, dataset generation, and TLF production with full traceability. LLMs are used selectively for contextual code generation, metadata enrichment, validation, and documentation — always grounded in structured metadata and deterministic outputs. Pilot implementations demonstrate significant reductions in programming timelines and audit-ready outputs suitable for regulatory submissions. An additional benefit of SPARC's unified platform is the seamless handoff it enables between biostatistics, programming, and medical writing teams, reducing workflow friction while preserving end-to-end traceability.
This paper outlines the architecture, design principles, and practical deployment strategies of hybrid AI in clinical data engineering, illustrating why metadata-centric, LLM-augmented automation represents a sustainable future for regulatory-compliant clinical trial workflows.
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The complete paper — architecture, design principles, and deployment strategy — is available as a PDF above.