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Introduction: Artificial intelligence tools such as the large language models (LLMs) Bard and ChatGPT have generated significant research interest. Utilization of these LLMs to study the epidemiology of a target population could benefit urologists. We investigated whether Bard and ChatGPT can perform a large-scale calculation of the incidence and prevalence of kidney stone disease. Materials and Methods: We obtained reference values from two published studies, which used the National Health and Nutrition Examination Survey (NHANES) database to calculate the prevalence and incidence of kidney stone disease. We then tested the capability of Bard and ChatGPT to perform similar calculations using two different methods. First, we instructed the LLMs to access the data sets and independently perform the calculation. Second, we instructed the interfaces to generate a customized computer code, which could perform the calculation on downloaded data sets. Results: While ChatGPT denied the ability to access and perform calculations on the NHANES database, Bard intermittently claimed the ability to do so. Bard provided either accurate results or inaccurate and inconsistent results. For example, Bard's "calculations" for the incidence of kidney stones from 2015 to 2018 were 2.1% (95% CI 1.5–2.7), 1.75% (95% CI 1.6–1.9), and 0.8% (95% CI 0.7–0.9), while the published number was 2.1% (95% CI 1.5–2.7). Bard provided discrete mathematical details of its calculations, however, when prompted further, admitted to having obtained the numbers from online sources, including our chosen reference articles, rather than from a de novo calculation. Both LLMs were able to produce a code (Python) to use on the downloaded NHANES data sets, however, these would not readily execute. Conclusions: ChatGPT and Bard are currently incapable of performing epidemiologic calculations and lack transparency and accountability. Caution should be used, particularly with Bard, as claims of its capabilities were convincingly misleading, and results were inconsistent. [ABSTRACT FROM AUTHOR]

Objectives: To assess bone‐density testing (BDT) use amongst prostate cancer survivors receiving androgen‐deprivation therapy (ADT), and downstream implications for osteoporosis and fracture diagnoses, as well as pharmacological osteoporosis treatment in a national integrated delivery system. Patients and methods: We identified 17 017 men with prostate cancer who received any ADT between 2005 and 2014 using the Veterans Health Administration cancer registry and administrative data. We identified claims for BDT within a 3‐year period of ADT initiation. We then used multivariable regression to examine the association between BDT use and incident osteoporosis, fracture, and use of pharmacological treatment. Results: We found that a minority of patients received BDT (n = 2 502, 15%); however, the rate of testing increased to >20% by the end of the study period. Men receiving BDT were older at diagnosis and had higher‐risk prostate cancer (both P < 0.001). Osteoporosis and fracture diagnoses, use of vitamin D ± calcium, and bisphosphonates were all more common in men who received BDT. After adjustment, BDT, and to a lesser degree ≥2 years of ADT, were both independently associated with incident osteoporosis, fracture, and osteoporosis treatment. Conclusions: BDT is rare amongst patients with prostate cancer treated with ADT in this integrated delivery system. However, BDT was associated with substantially increased treatment of osteoporosis indicating an underappreciated burden of osteoporosis amongst prostate cancer survivors initiating ADT. Optimising BDT use and osteoporosis management in this at‐risk population appears warranted. [ABSTRACT FROM AUTHOR]