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AI-Assisted Summarization of Radiologic Reports: Evaluating GPT3davinci, BARTcnn, LongT5booksum, LEDbooksum, LEDlegal, and LEDclinical

Aichi Chien, Hubert Tang, Bhavita Jagessar, Kai-wei Chang, Nanyun Peng, Kambiz Nael, and Noriko Salamon, in American Journal of Neuroradiology, 2024.

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Abstract

The review of clinical reports is an essential part of monitoring disease progression. Synthesizing multiple imaging reports is also important for clinical decisions. It is critical to aggregate information quickly and accurately. Machine learning natural language processing (NLP) models hold promise to address an unmet need for report summarization. We evaluated NLP methods to summarize longitudinal aneurysm reports. A total of 137 clinical reports and 100 PubMed case reports were used in this study. Models were compared against expert-generated summaries using longitudinal imaging notes collected in our institute and compared using publicly accessible PubMed case reports.

Bib Entry

@inproceedings{chien2024aiassisted,
  title = {AI-Assisted Summarization of Radiologic Reports: Evaluating GPT3davinci, BARTcnn, LongT5booksum, LEDbooksum, LEDlegal, and LEDclinical},
  author = {Chien, Aichi and Tang, Hubert and Jagessar, Bhavita and Chang, Kai-wei and Peng, Nanyun and Nael, Kambiz and Salamon, Noriko},
  year = {2024},
  booktitle = {American Journal of Neuroradiology}
}

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