BACKGROUND The rapid development of artificial intelligence (AI) has facilitated access to medical information. The most well-known AI model today is ChatGPT, developed by OpenAI, and its use in various medical disciplines has become increasingly widespread. The reliability of this system in providing disease-specific clinical guidance in urologic oncology remains uncertain. OBJECTIVE To investigate the performance of ChatGPT-5 on kidney cancer treatment, staging, and follow-up modalities using a real patient population. METHODS A total of 211 patients who underwent radical and partial nephrectomy at our clinic between 2014 and 2025 were retrospectively analyzed. Preop symptoms, tumor size, location, R.E.N.A.L. scores, planned and performed surgical method, tumor pathological type, size, TNM staging, and Leibovich scores for clear cell tumors were recorded. All data were then imported into ChatGPT-5, and the results were recorded and compared for their compatibility with actual clinical outcomes. RESULTS Among 211 patients (mean age: 58.2 years; 65.8% male), 113 underwent radical nephrectomy and 98 underwent partial nephrectomy. ChatGPT-5 recommended radical nephrectomy in 57 cases and partial nephrectomy in 68 cases, demonstrating moderate agreement with clinical decisions (Cohen's Kappa = 0.410, p<0.001). It demonstrated 85.9% accuracy in TNM classification and 76.3% consistency in Leibovich scoring for clear cell RCC. AI tended to favor nephron-sparing approaches more frequently than clinicians, particularly in mid-pole tumors and in cases with low R.E.N.A.L. scores. CONCLUSIONS ChatGPT-5 demonstrates promising potential as a support tool for the treatment, follow-up, and staging of kidney cancer. However, the variability in surgical recommendations highlights the need for continuous improvement and physician oversight. Before full clinical integration, multicenter, comparative studies between different AI models are required.

Background/Objectives: To compare the results of oral chemolysis of non-opaque and semi-opaque kidney stones using potassium citrate (PS) and allopurinol + potassium citrate (ALPS) prospectively without advanced metabolic analysis. Methods: Between 2020 and 2022, 59 patients aged 19–60 years with non-obstructive, semi-opaque, and non-opaque kidney stones of 8–13 mm in size detected by non-contrast tomography were investigated, and oral chemolysis (potassium citrate 10 mEq 2 × 2, allopurinol 10 mEq 2 × 2) was initiated. Patients were divided into two groups, potassium citrate (PS) and allopurinol + potassium citrate (ALPS), according to the treatment to be received. The essential characteristics of the groups, monthly laboratory values throughout the process, and the stone size measured by non-contrast tomography at the initial presentation and the stone size after a mean of 9–10 months of treatment was recorded and compared. Results: The inferior calyx was the most common location. There were no statistically significant differences between the groups with regard to age, sex, size, location or side. Before treatment, the mean stone size was 11.01 ± 2.27 mm in the potassium citrate group and 11.1 ± 2.03 mm in the other group. Following treatment, both groups changed significantly, but did not differ statistically (p < 0.001) (p = 0.281). The mean urine pH of all patients changed considerably after treatment (p < 0.001). Conclusions: Potassium citrate-based urinary alkalisation can be started for non-opaque and semi-opaque kidney stones without metabolic analysis if the urine pH is appropriate. The combination of allopurinol with potassium citrate has no therapeutic advantage.