Checkpoint-Augmented Clinical Reasoning: Applying Recoverable Program-of-Thought Frameworks to Medical Diagnostic Pipelines

Abstract

Medical diagnostic reasoning demands both systematic rigor and robust error recovery, yet contemporary large language model (LLM) pipelines frequently lack mechanisms to detect and correct intermediate inferential failures before they propagate into final clinical recommendations. We introduce \textbf{Checkpoint-Augmented Clinical Reasoning} (CACR), a novel framework that embeds structured verification checkpoints within a recoverable Program-of-Thought (PoT) architecture, enabling transparent, auditable, and self-correcting diagnostic chains across multi-step clinical decision pipelines.

CACR operationalizes diagnostic reasoning as a sequence of formally defined computational steps, each guarded by a lightweight verifier module that evaluates logical consistency, evidential sufficiency, and differential plausibility prior to downstream propagation. When a checkpoint detects an anomalous or contradictory state, the framework initiates a targeted rollback to the nearest valid reasoning node, resamples the affected inference segment, and resumes forward execution---preserving both computational efficiency and clinical safety. This recoverable execution model draws inspiration from fault-tolerant distributed systems and adapts their guarantees to the probabilistic, knowledge-intensive domain of clinical medicine.

We evaluate CACR on three benchmark diagnostic corpora spanning internal medicine, rare disease identification, and emergency triage, demonstrating statistically significant improvements in diagnostic accuracy, reasoning coherence, and hallucination suppression relative to chain-of-thought and standard PoT baselines. Notably, CACR reduces critical inferential errors by approximately 34\% while incurring only modest additional latency overhead.

These findings suggest that checkpoint-augmented recoverable reasoning architectures represent a principled and practically deployable strategy for elevating the reliability of LLM-driven clinical decision support, with broader implications for safety-critical AI deployment in high-stakes professional domains.