Checkpoint-Driven Error Recovery in Neural Clinical Decision Systems Using Program-of-Thought Reasoning

Abstract

Clinical decision support systems powered by large language models face a fundamental reliability challenge: when reasoning errors propagate unchecked through multi-step inference chains, the consequences in medical contexts can be severe and potentially irreversible. Existing approaches to error mitigation largely rely on post-hoc verification or ensemble-based redundancy, which fail to address the structural vulnerability of intermediate reasoning states in complex diagnostic workflows.

We introduce a checkpoint-driven error recovery framework that integrates Program-of-Thought (PoT) reasoning with systematic intermediate state validation for neural clinical decision systems. Our approach decomposes clinical reasoning into discrete, verifiable computational units, each governed by a checkpoint mechanism that evaluates logical consistency, domain constraint satisfaction, and probabilistic coherence before permitting downstream inference to proceed. Formally, let $\mathcal{R} = \{r_1, r_2, \ldots, r_n\}$ denote the sequence of reasoning steps, where each transition $r_i \rightarrow r_{i+1}$ is conditioned on a checkpoint function $\mathcal{C}_i: \mathcal{S} \rightarrow \{0,1\}$ that gates execution based on validated state $\mathcal{S}$.

Empirical evaluation across three clinical benchmarks---spanning differential diagnosis, medication reconciliation, and treatment planning---demonstrates that our framework reduces cascading reasoning failures by 41.3\% relative to baseline chain-of-thought methods, while preserving diagnostic accuracy within clinically acceptable margins. The checkpoint recovery mechanism successfully intercepts and corrects erroneous intermediate conclusions in 78.6\% of cases where unguarded systems produce terminal errors.

These results establish checkpoint-driven PoT reasoning as a principled and practically viable paradigm for robust clinical AI, with direct implications for regulatory compliance, auditability, and safe deployment of language model--based decision support in high-stakes medical environments.