Summarizing clinical evidence utilizing large language models for cancer treatments: a blinded comparative analysis
Claude performed at a consistently higher level than other LLMs, all tested LLMs required careful editing from a domain expert to become usable. More time will be needed to determine the suitability of LLMsto independently generate clinical synopses.
Authors
- Aleenah Mohsin (ORCID: https://orcid.org/0000-0002-3441-619X)
- Rahul Banerjee (ORCID: https://orcid.org/0000-0001-9555-7984)
- Jeremy L. Warner (ORCID: https://orcid.org/0000-0002-2851-7242)
- Mary Kwok (ORCID: https://orcid.org/0000-0002-3002-8130)
- Sanjay Mishra (ORCID: https://orcid.org/0000-0002-7775-9600)
- Will Ma
- Samuel M. Rubinstein (ORCID: https://orcid.org/0000-0002-5568-4441)
- Andrew J. Cowan
- Peter Yang (ORCID: https://orcid.org/0000-0002-1870-7464)
Institutions
- University of North Carolina at Chapel Hill (US)
- Princeton University (US)
- University of Washington (US)
- Brown University (US)
- Rhode Island Hospital (US)
- Massachusetts General Hospital (US)
- Fred Hutch Cancer Center (US)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2025-04-29
- DOI
- https://doi.org/10.3389/fdgth.2025.1569554
- Citations
- 2
- Primary Topic
- Lung Cancer Treatments and Mutations
- Type
- article
- Field-Weighted Citation Impact
- 3.04