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Wire-Calibrated Induction of Segmental Ureteral Strictures in Rats <em>via</em> Midline Laparotomy

Ureteral strictures remain a major challenge in reconstructive urology and can arise from iatrogenic injury, chronic inflammation, trauma, congenital ...

standardized-ileal-bladder-augmentation-for-enterocystoplasty-rats

Standardized Ileal Bladder Augmentation For Enterocystoplasty In Rats via Midline Laparotomy

Bladder augmentation using intestinal segments remains a cornerstone reconstructive procedure for patients with low-capacity or poorly compliant bladders ...

Medical Faculty of the University of Heidelberg

German Cancer Research Center (DKFZ) Heidelberg

DKFZ Hector Cancer Institute at the University Medical Center Mannheim

University Medical Center Mannheim, Medical Faculty of the University of Heidelberg

Large language models (LLMs) are increasingly explored as chatbots for patient education, including applications in urooncology. Since only 12% of adults have proficient health literacy and most patient information materials exceed recommended reading levels, improving readability is crucial. Although LLMs could potentially increase the readability of medical information, evidence is mixed, underscoring the need to assess chatbot outputs in clinical settings. Therefore, this study evaluates the measured and perceived readability of chatbot responses in speech-based interactions with urological patients. Urological patients engaged in unscripted conversations with a GPT-4-based chatbot. Transcripts were analyzed using three readability indices: Flesch-Reading-Ease (FRE), Lesbarkeitsindex (LIX) and Wiener-Sachtextformel (WSF). Perceived readability was assessed using a survey covering technical language, clarity and explainability. Associations between measured and perceived readability were analyzed. Knowledge retention was not assessed in this study. A total of 231 conversations were evaluated. The most frequently addressed topics were prostate cancer (22.5%), robotic-assisted prostatectomy (19.9%) and follow-up (18.6%). Objectively, responses were classified as difficult to read (FRE 43.1 ± 9.1; LIX 52.8 ± 6.2; WSF 11.2 ± 1.6). In contrast, perceived readability was rated highly for technical language, clarity and explainability (83-90%). Correlation analyses revealed no association between objective and perceived readability. Chatbot responses were objectively written at a difficult reading level, exceeding recommendations for optimized health literacy. Nevertheless, most patients perceived the information as clear and understandable. This discrepancy suggests that perceived comprehensibility is influenced by factors beyond measurable linguistic complexity.

ArticleTotal : 2	Year
Publication title	2026
Publication title	2026

Luisa Renner

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