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Can Artificial Intelligence-Based ChatGPT Enhance Patient Education in Undescended Testis Cases? (Preprint)

2026·0 ZitationenOpen Access
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11

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2026

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Abstract

<sec> <title>BACKGROUND</title> Artificial Intelligence (AI), a multidisciplinary approach, aims to automate tasks that require human input. ChatGPT, an AI tool, is popular among medical students and professionals, particularly in clinical applications like undescended testis, a congenital anomaly affecting fertility and long-term health. However, evaluating ChatGPT's accuracy and consistency is crucial for its effectiveness. </sec> <sec> <title>OBJECTIVE</title> This study investigates ChatGPT’s ability to provide accurate, accessible, and culturally appropriate educational content about undescended testis (cryptorchidism). </sec> <sec> <title>METHODS</title> ChatGPT 4.0 was used to answer 15 structured questions about diagnosis, treatment, and postoperative care for undescended testis. The Suitability Assessment of Materials tool evaluated responses based on content quality, literacy demand, and cultural relevance. Nine pediatric urologists participated in the assessment, converting scores into percentages. </sec> <sec> <title>RESULTS</title> ChatGPT achieved an overall SAM score of 77.62%, categorizing the material as superior. Diagnosis averaged 72.26%, treatment scored the highest at 79.87%, and postoperative care reached 80.76%. All questions across domains surpassed the 70% threshold for superior classification. However, some responses demonstrated variability due to incomplete contextual accuracy. </sec> <sec> <title>CONCLUSIONS</title> ChatGPT exhibits strong potential as a patient education tool for undescended testis, delivering superior responses in diagnosis, treatment, and postoperative care. To enhance reliability, continuous evaluation, refinement, and adherence to ethical guidelines are essential for improving AI-driven health education platforms. </sec>

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Artificial Intelligence in Healthcare and EducationTesticular diseases and treatmentsRadiomics and Machine Learning in Medical Imaging
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