Checkpoint-Guided Recovery of Clinical Reasoning Chains in Health-Focused Large Language Models

Abstract

Large language models (LLMs) deployed in clinical decision-support contexts have demonstrated considerable promise in replicating multi-step diagnostic reasoning; however, their susceptibility to reasoning chain degradation—wherein intermediate inferential steps become logically inconsistent or factually erroneous—poses a significant patient safety concern. Existing error-correction mechanisms largely operate post hoc at the output level, failing to address the propagation of faults through intermediate reasoning states. This paper introduces a novel framework termed \textit{Checkpoint-Guided Recovery} (CGR), designed to systematically detect, localize, and remediate breakdowns within clinical reasoning chains produced by health-focused LLMs.

The CGR framework operates by inserting structured verification checkpoints at semantically meaningful junctures within a model's chain-of-thought generation process. Each checkpoint applies a suite of clinically grounded validation criteria—including ontological consistency, differential plausibility, and pharmacological coherence—to assess the integrity of the evolving reasoning trace. Upon detection of a violation, the framework initiates a targeted rollback and re-generation procedure, restoring the chain to its last verified state before continuing inference under corrective constraints.

Empirical evaluation is conducted across three benchmark clinical reasoning datasets, encompassing internal medicine, emergency triage, and pharmacotherapy domains. The CGR framework achieves a statistically significant reduction in reasoning chain error rate of up to 34.7\% relative to standard chain-of-thought prompting baselines, while preserving diagnostic conclusion accuracy. Furthermore, human expert evaluations confirm marked improvements in the logical coherence and clinical defensibility of recovered reasoning traces.

These findings underscore the necessity of process-level, rather than purely output-level, quality assurance in clinical LLM deployment. The CGR framework represents a principled step toward verifiable and trustworthy AI-assisted clinical reasoning.