Radiology reports are invaluable for clinical decision-making and hold great potential for automated analysis when structured into machine-readable formats. These reports often contain uncertainty, which we categorize into two distinct types: (i) Explicit uncertainty reflects doubt about the presence or absence of findings, conveyed through hedging phrases. These vary in meaning depending on the context, making rule-based systems insufficient to quantify the level of uncertainty for specific findings; (ii) Implicit uncertainty arises when radiologists omit parts of their reasoning, recording only key findings or diagnoses. Here, it is often unclear whether omitted findings are truly absent or simply unmentioned for brevity. We address these challenges with a two-part framework. We quantify explicit uncertainty by creating an expert-validated, LLM-based reference ranking of common hedging phrases, and mapping each finding to a probability value based on this reference. In addition, we model implicit uncertainty through an expansion framework that systematically adds characteristic sub-findings derived from expert-defined diagnostic pathways for 14 common diagnoses. Using these methods, we release Lunguage++, an expanded, uncertainty-aware version of the Lunguage benchmark of fine-grained structured radiology reports. This enriched resource enables uncertainty-aware image classification, faithful diagnostic reasoning, and new investigations into the clinical impact of diagnostic uncertainty.

翻译：放射学报告对临床决策具有重要价值，当被结构化为机器可读格式时，在自动化分析方面展现出巨大潜力。这些报告常包含不确定性，我们将其分为两种不同类型：（i）显式不确定性反映了对发现存在与否的怀疑，通过模糊限制语表达。这些短语的含义随语境变化，使得基于规则的系统不足以量化特定发现的不确定性水平；（ii）隐式不确定性产生于放射科医生省略部分推理过程，仅记录关键发现或诊断的情况。此时，通常难以判断被省略的发现是确实不存在，还是仅为简洁起见未被提及。我们通过一个双部分框架应对这些挑战。我们通过创建专家验证的、基于LLM的常见模糊限制语参考排序，并根据该参考将每个发现映射到概率值，从而量化显式不确定性。此外，我们通过扩展框架对隐式不确定性进行建模，该框架系统性地添加了源自专家定义的14种常见诊断路径的特征性子发现。运用这些方法，我们发布了Lunguage++——细粒度结构化放射学报告基准Lunguage的扩展版，该版本具备不确定性感知能力。这一增强资源支持不确定性感知的图像分类、忠实于临床的诊断推理，以及对诊断不确定性的临床影响的新研究。