Evaluating large language models for lay summaries of radiology reports using tailored prompting strategies and mixed-method assessment
- DOI
- 10.1371/journal.pdig.0001672
- Published
- 2026-09-17
- Container
- PLOS Digital Health
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pdig.0001672,
title = {Evaluating large language models for lay summaries of radiology reports using tailored prompting strategies and mixed-method assessment},
author = {Nanziba Tasneem and Christian B. van der Pol and Ambreen Zahoor and Nitin Juggath and Kyle McGowan and Cynthia Lokker and Ashirbani Saha},
year = {2026},
journal = {PLOS Digital Health},
doi = {10.1371/journal.pdig.0001672},
url = {https://doi.org/10.1371/journal.pdig.0001672}
}RIS
TY - JOUR TI - Evaluating large language models for lay summaries of radiology reports using tailored prompting strategies and mixed-method assessment AU - Nanziba Tasneem AU - Christian B. van der Pol AU - Ambreen Zahoor AU - Nitin Juggath AU - Kyle McGowan AU - Cynthia Lokker AU - Ashirbani Saha PY - 2026 JO - PLOS Digital Health DO - 10.1371/journal.pdig.0001672 UR - https://doi.org/10.1371/journal.pdig.0001672 ER -
APA
Tasneem, N., Pol, C. B. V. D., Zahoor, A., Juggath, N., McGowan, K., Lokker, C., & Saha, A. (2026). Evaluating large language models for lay summaries of radiology reports using tailored prompting strategies and mixed-method assessment. PLOS Digital Health. https://doi.org/10.1371/journal.pdig.0001672
Source records
- crossref · retrieved 2026-09-25T03:21:12.994Z