@inproceedings{van-veen-etal-2023-radadapt,
    title = "{R}ad{A}dapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models",
    author = "Van Veen, Dave  and
      Van Uden, Cara  and
      Attias, Maayane  and
      Pareek, Anuj  and
      Bluethgen, Christian  and
      Polacin, Malgorzata  and
      Chiu, Wah  and
      Delbrouck, Jean-Benoit  and
      Zambrano Chaves, Juan  and
      Langlotz, Curtis  and
      Chaudhari, Akshay  and
      Pauly, John",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bionlp-1.42",
    doi = "10.18653/v1/2023.bionlp-1.42",
    pages = "449--460",
    abstract = "We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical text, or clinical text) and via discrete prompting or parameter-efficient fine-tuning. Our results consistently achieve best performance by maximally adapting to the task via pretraining on clinical text and fine-tuning on RRS examples. Importantly, this method fine-tunes a mere 0.32{\%} of parameters throughout the model, in contrast to end-to-end fine-tuning (100{\%} of parameters). Additionally, we study the effect of in-context examples and out-of-distribution (OOD) training before concluding with a radiologist reader study and qualitative analysis. Our findings highlight the importance of domain adaptation in RRS and provide valuable insights toward developing effective natural language processing solutions for clinical tasks.",
}

We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical text, or clinical text) and via discrete prompting or parameter-efficient fine-tuning. Our results consistently achieve best performance by maximally adapting to the task via pretraining on clinical text and fine-tuning on RRS examples. Importantly, this method fine-tunes a mere 0.32{\%} of parameters throughout the model, in contrast to end-to-end fine-tuning (100{\%} of parameters). Additionally, we study the effect of in-context examples and out-of-distribution (OOD) training before concluding with a radiologist reader study and qualitative analysis. Our findings highlight the importance of domain adaptation in RRS and provide valuable insights toward developing effective natural language processing solutions for clinical tasks.
Van Veen, Dave Van Uden, Cara Attias, Maayane Pareek, Anuj Bluethgen, Christian Polacin, Malgorzata Chiu, Wah Delbrouck, Jean-Benoit Zambrano Chaves, Juan Langlotz, Curtis Chaudhari, Akshay Pauly, John
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models