JCO clinical cancer informatics · 2025
Enhancing Readability of Lay Abstracts and Summaries for Urologic Oncology Literature Using Generative Artificial Intelligence: BRIDGE-AI 6 Randomized Controlled Trial.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Ganjavi Conner, Layne Ethan, Cei Francesco, Gill Karanvir, Magoulianitis Vasileios, Abreu Andre, Goldenberg Mitchell, Desai Mihir M, Gill Inderbir, Cacciamani Giovanni E
Аннотация
PURPOSE: To 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.
METHODS: Forty 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.
RESULTS: Mean 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).
CONCLUSION: 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.
METHODS: Forty 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.
RESULTS: Mean 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).
CONCLUSION: 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.