Generative artificial intelligence (AI) is a promising direction for augmenting clinical diagnostic decision support and reducing diagnostic errors, a leading contributor to medical errors. To further the development of clinical AI systems, the Diagnostic Reasoning Benchmark (DR.BENCH) was introduced as a comprehensive generative AI framework, comprised of six tasks representing key components in clinical reasoning. We present a comparative analysis of in-domain versus out-of-domain language models as well as multi-task versus single task training with a focus on the problem summarization task in DR.BENCH (Gao et al., 2023). We demonstrate that a multi-task, clinically trained language model outperforms its general domain counterpart by a large margin, establishing a new state-of-the-art performance, with a ROUGE-L score of 28.55. This research underscores the value of domain-specific training for optimizing clinical diagnostic reasoning tasks.

翻译：生成式人工智能（AI）是增强临床诊断决策支持、减少诊断错误（医疗差错的主要原因之一）的一个有前景的方向。为进一步推动临床AI系统的发展，诊断推理基准（DR.BENCH）作为一套全面的生成式AI框架被提出，包含代表临床推理关键组成部分的六项任务。我们针对DR.BENCH中的问题总结任务（Gao等人，2023），开展了领域内与领域外语言模型以及多任务与单任务训练的对比分析。研究表明，经过临床预训练的多任务语言模型在性能上大幅超越通用领域模型，以ROUGE-L得分28.55创下新的最优成绩。本研究强调了领域特定训练对于优化临床诊断推理任务的重要价值。