Benign prostatic hyperplasia (BPH) is the most prevalent urological disease in men that leads to lower urinary tract symptoms (LUTS). The clinical presentation is, most likely, obstructive symptoms such as intermittency, hesitancy and poor stream, due to the obstructive nature of the pathology. BPH treatment approach varies. However, they can be divided into two main approaches which are non-surgical and surgical. Non-surgical methods usually started first, such as lifestyle modifications, watchful waiting, and medications. Hence, surgical intervention remains the mainstay of treatment to relieve clinical symptoms. Although transurethral resection of the prostate (TURP) is the gold standard, management is shifting towards minimally invasive surgeries such as Rezūm due to its good outcome and fewer adverse effects. We present a case of prostatic tissue sloughing, a rare complication post Rezūm system therapy in a 50-year-old male.

Background/Objectives: Bladder cancer, the most common and heterogeneous malignancy of the urinary tract, presents with diverse types and treatment options, making comprehensive patient education essential. As large language models (LLMs) emerge as a promising resource for disseminating medical information, their accuracy and validity compared to traditional methods remain under-explored. This study aims to evaluate the effectiveness of LLMs in educating the public about bladder cancer. Methods: Frequently asked questions regarding bladder cancer were sourced from reputable educational materials and assessed for accuracy, comprehensiveness, readability, and consistency by two independent board-certified urologists, with a third resolving any discrepancies. The study utilized a 3-point Likert scale for accuracy, a 5-point Likert scale for comprehensiveness, and the Flesch–Kincaid (FK) Grade Level and Flesch Reading Ease (FRE) scores to gauge readability. Results: ChatGPT-3.5, ChatGPT-4, and Gemini were evaluated on 12 general questions, 6 questions related to diagnosis, 28 concerning treatment, and 7 focused on prevention. Across all categories, the correct response rate was notably high, with ChatGPT-3.5 and ChatGPT-4 achieving 92.5%, compared to 86.3% for Gemini, with no significant difference in accuracy. However, there was a significant difference in comprehensiveness (p = 0.011) across the models. Overall, a significant difference in performance was observed among the LLMs (p < 0.001), with ChatGPT-4 providing the most college-level responses, though these were the most challenging to read. Conclusions: In conclusion, our study adds value to the applications of Artificial Intelligence (AI) in bladder cancer education, with notable insights into the accuracy, comprehensiveness, and stability of the three LLMs.

Background: Bladder cancer, the most common and heterogeneous malignancy of the urinary tract, presents with diverse types and treatment options, making comprehensive patient education essential. As Large Language Models (LLMs) emerge as a promising resource for disseminating medical information, their accuracy and validity compared to traditional methods remain under-explored. This study aims to evaluate the effectiveness of LLMs in educating the public about bladder cancer. Methods: Frequently asked questions regarding bladder cancer were sourced from reputable educational materials and assessed for accuracy, comprehensiveness, readability, and consistency by two independent board-certified urologists, with a third resolving any discrepancies. The study utilized a 3-point Likert scale for accuracy, a 5-point Likert scale for comprehensiveness, and the Flesch-Kincaid FK Grade Level and Flesch Reading Ease (FRE) scores to gauge readability. Results: ChatGPT-3.5, ChatGPT-4, and Gemini were evaluated on 12 general questions, 6 related to diagnosis, 28 concerning treatment, and 7 focused on prevention. Across all categories, the correct response rate was notably high, with ChatGPT-3.5 and ChatGPT-4 achieving 92.5%, compared to 86.3% for Gemini, with no significant difference in accuracy. However, there was a significant difference in comprehensiveness (p = 0.011) across the models. Overall, a significant difference in performance was observed among the LLMs (p < 0.001), with ChatGPT-4 providing the most college-level responses, though these were the most challenging to read. Conclusion: In conclusion, our study adds value to the applications of AI in bladder cancer education with notable insights on the accuracy, comprehensiveness, and stability of the three LLMs.