Scalable Clinical Oversight of Large Language Models Via Uncertainty Triangulation

Overview

This prospective, multi-reader, randomized crossover trial evaluates SCOUT (Scalable Clinical Oversight via Uncertainty Triangulation), a model-agnostic meta-verification framework that selectively defers unreliable large language model (LLM) predictions to clinicians by triangulating three orthogonal uncertainty signals: model heterogeneity, stochastic inconsistency, and reasoning critique. The trial assesses whether SCOUT-assisted review can reduce physician review time compared with standard manual review of AI-generated diagnoses while maintaining non-inferior diagnostic accuracy in coronary heart disease (CHD) subtyping.

Background: Large language models are increasingly deployed in clinical workflows, yet requiring clinician review of every AI output negates the efficiency gains that motivate their adoption. SCOUT addresses this efficiency-safety paradox through algorithmic meta-verification. The SCOUT framework triangulates three orthogonal external signals to determine case-level uncertainty: (1) Model Heterogeneity - whether a structurally different auxiliary LLM agrees with the primary model; (2) Stochastic Inconsistency - whether repeated sampling from the same model yields divergent outputs; (3) Reasoning Critique - whether an external checker model identifies logical flaws in the chain-of-thought reasoning. In this crossover trial, 7 clinicians of varying seniority (2 junior residents, 3 senior residents, 2 attending physicians) each review all 110 cases under both standard manual review and SCOUT-assisted review workflows. The study evaluates workflow efficiency (primary endpoint) and diagnostic accuracy (secondary endpoint).