An Agentic LLM Pipeline for Structured Adaptation of Clinical AI Literature: Construction and Validation of HOps

Actionable clinical AI guidance is dispersed across hundreds of heterogeneous, unstructured PDF documents spanning reporting guidelines, implementation science frameworks, and digital health evaluation tools. We present a six-agent sequential LLM pipeline that converts this unstructured medical literature into a structured, machine-readable tabular dataset at scale. Applied to 55 clinical AI framework documents, the pipeline extracted 2,386 classified items mapped to eight predefined lifecycle stages, revealing systematic coverage gaps across the literature. This structured synthesis directly informed the development of HOps, a 20-question guiding framework for hospital digital product development, which achieved 87% endorsement and 67% questionlevel agreement across five domain experts. Our work demonstrates that agentic structured adaptation of clinical literature is feasible, produces reproducible outputs, and yields structured datasets with direct utility for clinical AI governance and deployment research.