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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 ( > .05). Quality scores ranged from 85% to 100%, with hallucinations in 1% of sections. The best test showed significantly better readability (68.9 25.3; < .001), grade level, and text metrics compared with the OA. Methods sections had slightly lower accuracy (85% 100%; = .03) and trifecta achievement (82.5% 100%; = .01), but other sections retained high quality (≥92.5%; > .05). GAI-generated LAS recipients scored significantly better in comprehension and most perception-based questions ( < .001) with LAS being the only consistently significant predictor ( < .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.
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http://dx.doi.org/10.1200/CCI-25-00042 | DOI Listing |
Urol Oncol
September 2025
Nutritional, Genes and Human Disease Laboratory, Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh. Electronic address:
Background: Understanding the mutational landscape is critical for elucidating the molecular mechanisms driving cancer progression. This study aimed to profile somatic mutations in bladder cancer patients (N=7) from Bangladesh to provide insights into the genetic alterations underlying this malignancy.
Methods: We performed targeted sequencing of 50 oncogenes and tumor suppressor genes using the Ion AmpliSeq Cancer Hotspot Panel v2 on tumor and matched blood samples from seven bladder cancer patients.
Urol Oncol
October 2025
Department of Urology, Mayo Clinic, Rochester, MN. Electronic address:
Urol Oncol
October 2025
Department of Pathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center. Electronic address:
JCO Clin Cancer Inform
September 2025
USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA.
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.
JAMA
September 2025
Section of Urologic Oncology, Department of Urology, Michigan Medicine, Ann Arbor.