Leveraging GPT-4 for Post Hoc Transformation of Free-Text Radiology Reports into Structured Reporting: A Multilingual Feasibility Study.
By L. Adams, D. Truhn, Felix Busch, Avan Kader, S. Niehues, Marcus R. Makowski, K. Bressem. Radiology, 2023
302 citations recorded by the source corpus.
Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/b3189fe13fa7f8cddef2db697791b6b52c122dbd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Arun James Thirunavukarasu
- Laura Gutierrez
- Ting Fang Tan
- K. Elangovan
- D. Ting
- Darren S. J. Ting
Citations in the corpus
- Large language models in medicine (2023, background): “This illuminates avenues for implementation with more immediate promise than with clinical decision aids 53 .”
- MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data (2023, unknown): “Potential applications include aiding medical professionals in note-taking, composing discharge letters, retrieving information from extensive documents, summarizing content, and converting free-form texts into structured formats [2, 3].”