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18 January 2025 |
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Article overview
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Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes | | Sunjun Kweon
; Junu Kim
; Jiyoun Kim
; Sujeong Im
; Eunbyeol Cho
; Seongsu Bae
; Jungwoo Oh
; Gyubok Lee
; Jong Hak Moon
; Seng Chan You
; Seungjin Baek
; Chang Hoon Han
; Yoon Bin Jung
; Yohan Jo
; Edward Choi
; | | Date: |
1 Sep 2023 | | Abstract: | The development of large language models tailored for handling patients’
clinical notes is often hindered by the limited accessibility and usability of
these notes due to strict privacy regulations. To address these challenges, we
first create synthetic large-scale clinical notes using publicly available case
reports extracted from biomedical literature. We then use these synthetic notes
to train our specialized clinical large language model, Asclepius. While
Asclepius is trained on synthetic data, we assess its potential performance in
real-world applications by evaluating it using real clinical notes. We
benchmark Asclepius against several other large language models, including
GPT-3.5-turbo and other open-source alternatives. To further validate our
approach using synthetic notes, we also compare Asclepius with its variants
trained on real clinical notes. Our findings convincingly demonstrate that
synthetic clinical notes can serve as viable substitutes for real ones when
constructing high-performing clinical language models. This conclusion is
supported by detailed evaluations conducted by both GPT-4 and medical
professionals. All resources including weights, codes, and data used in the
development of Asclepius are made publicly accessible for future research. | | Source: | arXiv, 2309.00237 | | Services: | Forum | Review | PDF | Favorites | |
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