Scalable Clinical Oversight of Large Language Models Via Uncertainty Triangulation

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).

Inclusion criteria

• Board-certified or in-training cardiologists at Fuwai Hospital
• Spanning three experience strata: junior residents, senior residents, attending physicians

Exclusion criteria

• Clinicians involved in the development or optimization of the SCOUT framework
• Clinicians involved in the gold-standard adjudication process