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.

本研究的重点是使用 DR.BENCH 这一包含 6 个任务的综合生成 AI 框架对领域内和领域外语言模型进行比较分析，发现多任务、经过临床训练的语言模型在 DR.BENCH 中的问题总结任务中表现超过了其一般领域的对应物，取得了 28.55 的 ROUGE-L 得分，显示出了为优化临床诊断推理任务而进行领域特定的训练的价值。