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.
@article{arxiv.2306.04551,
title = {Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning},
author = {Brihat Sharma and Yanjun Gao and Timothy Miller and Matthew M. Churpek and Majid Afshar and Dmitriy Dligach},
journal= {arXiv preprint arXiv:2306.04551},
year = {2023}
}
Comments
Accepted to the Proceedings of the 5th Clinical NLP Workshop at ACL