The diagnosis and management of prostate cancer (PCa), the second most common cancer in men worldwide, are highly complex. Hence, patients often seek knowledge through additional resources, including AI chatbots such as ChatGPT and Google Bard. This study aimed to evaluate the performance of LLMs in providing education on PCa. Common patient questions about PCa were collected from reliable educational websites and evaluated for accuracy, comprehensiveness, readability, and stability by two independent board-certified urologists, with a third resolving discrepancy. Accuracy was measured on a 3-point scale, comprehensiveness was measured on a 5-point Likert scale, and readability was measured using the Flesch Reading Ease (FRE) score and Flesch–Kincaid FK Grade Level. A total of 52 questions on general knowledge, diagnosis, treatment, and prevention of PCa were provided to three LLMs. Although there was no significant difference in the overall accuracy of LLMs, ChatGPT-3.5 demonstrated superiority over the other LLMs in terms of general knowledge of PCa (p = 0.018). ChatGPT-4 achieved greater overall comprehensiveness than ChatGPT-3.5 and Bard (p = 0.028). For readability, Bard generated simpler sentences with the highest FRE score (54.7, p < 0.001) and lowest FK reading level (10.2, p < 0.001). ChatGPT-3.5, ChatGPT-4 and Bard generate accurate, comprehensive, and easily readable PCa material. These AI models might not replace healthcare professionals but can assist in patient education and guidance.

Prostate cancer, the second most common cancer in men worldwide, is highly complex regarding diagnosis and management. Hence, patients often seek knowledge through additional resources, including AI chatbots such as Generative Pre-trained Transformers (ChatGPT) and Google Bard. This study aimed to evaluate the performance of LLMs in providing educational content on prostate cancer. Common patient questions about prostate cancer were collected from reliable educational websites and evaluated for accuracy, comprehensiveness, readability, and stability by two independent board-certified urologists, with a third resolving discrepancies. Accuracy was measured on a 3-point scale, comprehensiveness on a 5-point Likert scale, and readability using the Flesch Reading Ease (FRE) Score and Flesch–Kincaid FK Grade Level. A total of 52 questions on general knowledge, diagnosis, treatment, and prevention of prostate cancer were provided to three LLMs. Although there was no significant difference in the overall accuracy of LLMs, ChatGPT demonstrated superiority among the LLMs in the context of general knowledge of prostate cancer (p = 0.018). ChatGPT Plus achieved higher overall comprehensiveness than ChatGPT and Bard (p = 0.028). For readability, Bard generated simpler sentences with the highest FRE score (54.7, p < 0.001) and lowest FK Reading Level (10.2, p < 0.001). ChatGPT and Bard generate accurate, understandable, and easily readable material on prostate cancer. These AI models might not replace healthcare professionals but can assist in patient education and guidance.

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/Objectives: The use of direct oral anticoagulants (DOACs) in obese patients is scarcely studied despite having many advantages over warfarin. Consequently, this study aims to assess the real-world safety and effectiveness of apixaban compared to warfarin in treating atrial fibrillation (AF) in obese patients. Methods: A retrospective cohort study examined consecutive AF patients with a BMI of ≥ 30 kg/m² treated with apixaban or warfarin. Patients were started on these medications between January 2015 and December 2021. Efficacy outcomes included ischemic stroke and venous thromboembolism (VTE) occurrences, while safety outcomes encompassed bleeding incidents and mortality rates. Outcomes were assessed following propensity score matching. Results: We identified 876 patients treated with either apixaban (414) or warfarin (462). Their mean age was 76.9, with a mean CHA₂DS₂VASc score of 4.9 ± 1.97. After matching and compared to warfarin, apixaban was correlated with a lower incidence of all-cause mortality (19.7% vs. 33.7%, p < 0.001). The incidences of stroke, venous thromboembolism (VTE), and bleeding events were (4.7% vs. 4.7%, p = 1.000), (1.0% vs. 2.6%, p = 0.107), and (3.9% vs. 6.2%, p = 0.139), respectively. Using Cox-regression model, apixaban was associated with lower mortality risk (HR = 0.728, 95% CI: 0.55–0.97; p = 0.030) which remained significant after adjusting for the conventional cardiovascular risk factors and BMI values. Conclusions: Apixaban is associated with a trend of reduced incidence of thromboembolism among obese patients with atrial fibrillation and significantly lowers all-cause mortality. Despite earlier concerns, the use of apixaban is an effective and safe alternative to warfarin among obese patients with AF.

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.

Background/Objectives: Rising antimicrobial resistance threatens Helicobacter pylori eradication worldwide, yet real-world data on microbiologically confirmed treatment outcomes from the Middle East remain scarce. This study aimed to determine the confirmed eradication failure rate among treatment-naïve patients, identify independent predictors of failure, and characterize gaps in post-treatment testing practices. Methods: This retrospective cohort study included treatment-naïve adults diagnosed with H. pylori infection at King Abdulaziz Medical City in Riyadh between January 2015 and August 2023. The primary outcome was microbiologically confirmed eradication failure, defined exclusively as a positive post-treatment test of cure (urea breath test, stool antigen, or endoscopic biopsy). Exploratory logistic regression was used to identify factors associated with treatment failure. Results: Of 850 patients, 196 (23.1%) had a documented post-treatment eradication test. The confirmed failure rate was 33.7% (66/196). In multivariate analysis, atrophic gastritis (aOR 3.22, 95% CI: 1.29–8.02; p = 0.012) and pregnancy (aOR 12.76, 95% CI: 1.79–91.06; p = 0.011) were independently associated with failure. No treatment regimen was significantly associated with eradication outcome. Conclusions: One in three tested patients failed first-line eradication, and over three-quarters of treated patients never received a confirmatory test of cure. These findings support transitioning to bismuth-based quadruple therapy in line with current guidelines and local resistance data, mandating routine post-treatment eradication testing, and establishing antimicrobial susceptibility surveillance.