Deterministic Foundations, AI Acceleration- Lessons from Developing Hybrid Clinical Review Tools

Abstract

As large language models (LLMs) gain traction in clinical development, teams face a fundamental question - when should deterministic programming suffice, and when can probabilistic AI accelerate insight generation? We developed a medical review R Shiny application using a hybrid approach that combines robust, deterministic components for core analyses with AI-assisted exploration that accelerates medical reviewers' ability to address adhoc, novel questions with flexibility and velocity. This development journey revealed critical trade-offs—speed versus reproducibility, flexibility versus consistency, and breadth versus control—while highlighting the importance of transparency, human oversight and building (and maintaining) end user trust. We discuss practical consequences of the hybrid choice and propose suitability criteria based on problem definition, risk, criticality, and required traceability. Our experience demonstrates how clinical and development teams can harness both approaches while defining new ways of developing research tools that maintain the rigor required in regulated environments.