Vision-language models (VLMs) show promise in drafting radiology reports, yet they frequently suffer from logical inconsistencies, generating diagnostic impressions unsupported by their own perceptual findings or missing logically entailed conclusions. Standard lexical metrics heavily penalize clinical paraphrasing and fail to capture these deductive failures in reference-free settings. Toward guarantees for clinical reasoning, we introduce a neurosymbolic verification framework that deterministically audits the internal consistency of VLM-generated reports. Our pipeline autoformalizes free-text radiographic findings into structured propositional evidence, utilizing an SMT solver (Z3) and a clinical knowledge base to verify whether each diagnostic claim is mathematically entailed, hallucinated, or omitted. Evaluating seven VLMs across five chest X-ray benchmarks, our verifier exposes distinct reasoning failure modes, such as conservative observation and stochastic hallucination, that remain invisible to traditional metrics. On labeled datasets, enforcing solver-backed entailment acts as a rigorous post-hoc guarantee, systematically eliminating unsupported hallucinations to significantly increase diagnostic soundness and precision in generative clinical assistants.

翻译：视觉语言模型在起草放射学报告方面展现出潜力，但其经常存在逻辑不一致问题，例如生成的诊断印象缺乏自身感知发现的支持，或遗漏逻辑上必然的结论。传统的词汇匹配指标严重惩罚临床释义差异，且在无参考文本场景下无法捕捉这些演绎推理失败。为实现临床推理的保证，我们引入一种神经符号验证框架，可确定性审计视觉语言模型生成报告的内部一致性。该流程将自由文本的影像学发现自动形式化为结构化命题证据，利用SMT求解器（Z3）和临床知识库，以数学方式验证每个诊断主张是被严格蕴涵、幻觉生成还是被遗漏。通过在五个胸部X光基准测试中评估七个视觉语言模型，我们的验证器揭示了传统指标无法检测的典型推理失败模式，如保守性观察与随机性幻觉。在标注数据集上，强制执行求解器支持的蕴涵关系可作为严格的事后保证，系统性地消除无依据的幻觉生成，从而显著提升生成式临床辅助系统的诊断可靠性与精确度。