Enhancing Readability of Lay Abstracts and Summaries for Urologic Oncology Literature Using Generative Artificial Intelligence: BRIDGE-AI 6 Randomized Controlled Trial
GAI-generated LASs for urologic oncology research are highly readable and generally preserve the quality of the OAs. Patients and caregivers demonstrated improved comprehension and more favorable perceptions of LASs compared with OAs. Human oversight remains essential to ensure the accurate, complete, and clear representations of the original research.
Authors
- Vasileios Magoulianitis (ORCID: https://orcid.org/0009-0005-3907-1003)
- Karanvir Gill (ORCID: https://orcid.org/0000-0003-3437-269X)
- Mihir Desai (ORCID: https://orcid.org/0000-0002-3744-8234)
- Conner Ganjavi (ORCID: https://orcid.org/0000-0001-9698-964X)
- Inderbir S. Gill (ORCID: https://orcid.org/0000-0002-5113-7846)
- Giovanni Cacciamani (ORCID: https://orcid.org/0000-0002-8892-5539)
- Mitchell G. Goldenberg (ORCID: https://orcid.org/0000-0002-4601-5721)
- Andre Luis Abreu (ORCID: https://orcid.org/0000-0002-9167-2587)
- Francesco Cei (ORCID: https://orcid.org/0000-0001-7732-8651)
- Ethan Layne (ORCID: https://orcid.org/0009-0007-1639-5157)
Institutions
- University of Southern California (US)
- Artificial Intelligence in Medicine (Canada) (CA)
- Drexel University (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1200/cci-25-00042
- Citations
- 3
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.96