Evaluating the performance of Generative Pre-trained Transformer-4 (GPT-4) in standardizing radiology reports-Reference-Cited by-同舟云学术

Evaluating the performance of Generative Pre-trained Transformer-4 (GPT-4) in standardizing radiology reports

Published:2023-11-08 Issue: Volume: Page:
ISSN:1432-1084
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Hasani Amir M.,Singh Shiva,Zahergivar Aryan,Ryan Beth,Nethala Daniel,Bravomontenegro Gabriela,Mendhiratta Neil,Ball Mark,Farhadi Faraz,Malayeri Ashkan^ORCID

Funder

National Institutes of Health

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-023-10384-x.pdf

Reference23 articles.

1. Srinivasa Babu A, Brooks ML (2015) The malpractice liability of radiology reports: minimizing the risk. Radiographics 35:547–554

2. Larson DB (2018) Strategies for implementing a standardized structured radiology reporting program. Radiographics 38:1705–1716

3. Adams LC, Truhn D, Busch F et al (2023) Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study. Radiology. https://doi.org/10.1148/radiol.230725:230725

4. Jeblick K, Schachtner B, Dexl J et al (2023) ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports. Eur Radiol. https://doi.org/10.1007/s00330-023-10213-1

5. Gaube S, Suresh H, Raue M et al (2021) Do as AI say: susceptibility in deployment of clinical decision-aids. NPJ Digit Med 4:31

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