Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning

• URL: http://arxiv.org/abs/2306.04551v2
• Date: Tue, 13 Jun 2023 17:28:34 GMT
• Title: Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning
• Authors: Brihat Sharma, Yanjun Gao, Timothy Miller, Matthew M. Churpek, Majid Afshar and Dmitriy Dligach
• Abstract summary: We present a comparative analysis of in-domain versus out-of-domain language models as well as multi-task versus single task training. We demonstrate that a multi-task, clinically trained language model outperforms its general domain counterpart by a large margin.
• Score: 5.321587036724933
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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