Clinical decision-making is a dynamic, interactive, and cyclic process where doctors have to repeatedly decide on which clinical action to perform and consider newly uncovered information for diagnosis and treatment. Large Language Models (LLMs) have the potential to support clinicians in this process, however, most applications of LLMs in clinical decision support suffer from one of two limitations: Either they assume the unrealistic scenario of immediate availability of all patient information and do not model the interactive and iterative investigation process, or they restrict themselves to the limited "out-of-the-box" capabilities of large pre-trained models without performing task-specific training. In contrast to this, we propose to model clinical decision-making for diagnosis with a hypothesis-driven uncertainty-aware language agent, LA-CDM, that converges towards a diagnosis via repeatedly requesting and interpreting relevant tests. Using a hybrid training paradigm combining supervised and reinforcement learning, we train LA-CDM with three objectives targeting critical aspects of clinical decision-making: accurate hypothesis generation, hypothesis uncertainty estimation, and efficient decision-making. We evaluate our methodology on MIMIC-CDM, a real-world dataset covering four abdominal diseases containing various clinical tests and show the benefit of explicitly training clinical decision-making for increasing diagnostic performance and efficiency.

翻译：临床决策是一个动态、交互且循环的过程，医生需要反复决定执行何种临床操作，并利用新发现的信息进行诊断与治疗。大型语言模型（LLMs）具备支持临床医生完成这一过程的潜力，然而，当前LLMs在临床决策支持中的应用大多存在以下两种局限之一：要么假设所有患者信息均可即时获取（这一场景并不现实），且未对交互式、迭代式的诊疗过程进行建模；要么仅局限于利用大型预训练模型有限的"开箱即用"能力，而未进行针对特定任务的训练。与此不同，我们提出使用一种假设驱动、具备不确定性感知能力的语言智能体LA-CDM来建模诊断临床决策过程，该智能体通过反复申请并解读相关检测，逐步收敛至最终诊断。采用监督学习与强化学习相结合的混合训练范式，我们围绕临床决策的三个关键维度对LA-CDM进行训练：准确的假设生成、假设不确定性估计以及高效决策。我们在MIMIC-CDM数据集上评估了所提方法，该真实世界数据集涵盖四种腹部疾病并包含多种临床检测。实验结果表明，针对临床决策过程进行显式训练能够有效提升诊断性能与效率。