Journal article
Enhancing Readability of Lay Abstracts and Summaries for Urologic Oncology Literature Using Generative Artificial Intelligence: BRIDGE-AI 6 Randomized Controlled Trial
JCO clinical cancer informatics, v 9(9), 2500042
Sep 2025
PMID: 40929631
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
PURPOSETo evaluate a generative artificial intelligence (GAI) framework for creating readable lay abstracts and summaries (LASs) of urologic oncology research, while maintaining accuracy, completeness, and clarity, for the purpose of assessing their comprehension and perception among patients and caregivers. METHODSForty original abstracts (OAs) on prostate, bladder, kidney, and testis cancers from leading journals were selected. LASs were generated using a free GAI tool, with three versions per abstract for consistency. Readability was compared with OAs using validated metrics. Two independent reviewers assessed accuracy, completeness, and clarity and identified AI hallucinations. A pilot study was conducted with 277 patients and caregivers randomly assigned to receive either OAs or LASs and complete comprehension and perception assessments. RESULTSMean GAI-generated LAS generation time was <10 seconds. Across 600 sections generated, readability and quality metrics were consistent (P > .05). Quality scores ranged from 85% to 100%, with hallucinations in 1% of sections. The best test showed significantly better readability (68.9 v 25.3; P < .001), grade level, and text metrics compared with the OA. Methods sections had slightly lower accuracy (85% v 100%; P = .03) and trifecta achievement (82.5% v 100%; P = .01), but other sections retained high quality (>= 92.5%; P > .05). GAI-generated LAS recipients scored significantly better in comprehension and most perception-based questions (P < .001) with LAS being the only consistently significant predictor (P < .001). CONCLUSIONGAI-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.
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Details
- Title
- Enhancing Readability of Lay Abstracts and Summaries for Urologic Oncology Literature Using Generative Artificial Intelligence: BRIDGE-AI 6 Randomized Controlled Trial
- Creators
- Conner Ganjavi - University of Southern CaliforniaEthan Layne - University of Southern CaliforniaFrancesco Cei - University of Southern CaliforniaKaranvir Gill - University of Southern CaliforniaVasileios Magoulianitis - University of Southern CaliforniaAndre Abreu - University of Southern CaliforniaMitchell Goldenberg - University of Southern CaliforniaMihir M. Desai - University of Southern CaliforniaInderbir Gill - University of Southern CaliforniaGiovanni E. Cacciamani - University of Southern California
- Publication Details
- JCO clinical cancer informatics, v 9(9), 2500042
- Publisher
- Lippincott Williams & Wilkins
- Number of pages
- 10
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Medicine
- Web of Science ID
- WOS:001569124900001
- Scopus ID
- 2-s2.0-105015705542
- Other Identifier
- 991022197430104721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Oncology