Background: ChatGPT is a novel tool that allows people to engage in conversations with an advanced machine learning model. ChatGPT's performance in the US Medical Licensing Examination is comparable with a successful candidate's performance. However, its performance in the nephrology field remains undetermined. This study assessed ChatGPT's capabilities in answering nephrology test questions. Methods: Questions sourced from Nephrology Self-Assessment Program and Kidney Self-Assessment Program were used, each with multiple-choice single-answer questions. Questions containing visual elements were excluded. Each question bank was run twice using GPT-3.5 and GPT-4. Total accuracy rate, defined as the percentage of correct answers obtained by ChatGPT in either the first or second run, and the total concordance, defined as the percentage of identical answers provided by ChatGPT during both runs, regardless of their correctness, were used to assess its performance. Results: A comprehensive assessment was conducted on a set of 975 questions, comprising 508 questions from Nephrology Self-Assessment Program and 467 from Kidney Self-Assessment Program. GPT-3.5 resulted in a total accuracy rate of 51%. Notably, the employment of Nephrology Self-Assessment Program yielded a higher accuracy rate compared with Kidney Self-Assessment Program (58% versus 44%; P < 0.001). The total concordance rate across all questions was 78%, with correct answers exhibiting a higher concordance rate (84%) compared with incorrect answers (73%) (P < 0.001). When examining various nephrology subfields, the total accuracy rates were relatively lower in electrolyte and acid-base disorder, glomerular disease, and kidney-related bone and stone disorders. The total accuracy rate of GPT-4's response was 74%, higher than GPT-3.5 (P < 0.001) but remained below the passing threshold and average scores of nephrology examinees (77%). Conclusions: ChatGPT exhibited limitations regarding accuracy and repeatability when addressing nephrology-related questions. Variations in performance were evident across various subfields.

The accurate interpretation of CRRT machine alarms is crucial in the intensive care setting. ChatGPT, with its advanced natural language processing capabilities, has emerged as a tool that is evolving and advancing in its ability to assist with healthcare information. This study is designed to evaluate the accuracy of the ChatGPT-3.5 and ChatGPT-4 models in addressing queries related to CRRT alarm troubleshooting. This study consisted of two rounds of ChatGPT-3.5 and ChatGPT-4 responses to address 50 CRRT machine alarm questions that were carefully selected by two nephrologists in intensive care. Accuracy was determined by comparing the model responses to predetermined answer keys provided by critical care nephrologists, and consistency was determined by comparing outcomes across the two rounds. The accuracy rate of ChatGPT-3.5 was 86% and 84%, while the accuracy rate of ChatGPT-4 was 90% and 94% in the first and second rounds, respectively. The agreement between the first and second rounds of ChatGPT-3.5 was 84% with a Kappa statistic of 0.78, while the agreement of ChatGPT-4 was 92% with a Kappa statistic of 0.88. Although ChatGPT-4 tended to provide more accurate and consistent responses than ChatGPT-3.5, there was no statistically significant difference between the accuracy and agreement rate between ChatGPT-3.5 and -4. ChatGPT-4 had higher accuracy and consistency but did not achieve statistical significance. While these findings are encouraging, there is still potential for further development to achieve even greater reliability. This advancement is essential for ensuring the highest-quality patient care and safety standards in managing CRRT machine-related issues.

Background: The use of artificial intelligence (AI) for interpreting ambulatory blood pressure monitoring (ABPM) data is gaining traction in clinical practice. Evaluating the accuracy of AI models, like ChatGPT 4.0, in clinical settings can inform their integration into healthcare processes. However, limited research has been conducted to validate the performance of such models against expert interpretations in real-world clinical scenarios. Methods: A total of 53 ABPM records from Mayo Clinic, Minnesota, were analyzed. ChatGPT 4.0's interpretations were compared with consensus results from two experienced nephrologists, based on the American College of Cardiology/American Heart Association (ACC/AHA) guidelines. The study assessed ChatGPT's accuracy and reliability over two rounds of testing, with a three-month interval between rounds. Results: ChatGPT achieved an accuracy of 87% for identifying hypertension, 89% for nocturnal hypertension, 81% for nocturnal dipping, and 94% for abnormal heart rate. ChatGPT correctly identified all conditions in 60% of ABPM records. The percentage agreement between the first and second round of ChatGPT's analysis was 81% in identifying hypertension, 85% in nocturnal hypertension, 89% in nocturnal dipping, and 94% in abnormal heart rate. There was no significant difference in accuracy between the first and second round (all p > 0.05). The Kappa statistic was 0.63 for identifying hypertension, 0.66 for nocturnal hypertension, 0.76 for nocturnal dipping, and 0.70 for abnormal heart rate. Conclusions: ChatGPT 4.0 demonstrates potential as a reliable tool for interpreting 24-h ABPM data, achieving substantial agreement with expert nephrologists. These findings underscore the potential for AI integration into hypertension management workflows, while highlighting the need for further validation in larger, diverse cohorts.

Background: The digital transformation in medical education is reshaping how clinical skills, such as point-of-care ultrasound (POCUS), are taught. In nephrology fellowship programs, POCUS is essential for enhancing diagnostic accuracy, guiding procedures, and optimizing patient management. To address these evolving demands, we developed an artificial intelligence (AI)-driven POCUS curriculum using a multiagent approach that integrates human expertise with advanced AI models, thereby elevating educational standards and better preparing fellows for contemporary clinical practice. Methods: In April 2024, the Mayo Clinic Minnesota Nephrology Fellowship Program initiated a novel AI-assisted process to design a comprehensive POCUS curriculum. This process integrated multiple advanced AI models—including GPT-4.0, Claude 3.0 Opus, Gemini Advanced, and Meta AI with Llama 3—to generate initial drafts and iteratively refine content. A panel of blinded nephrology POCUS experts subsequently reviewed and modified the AI-generated material to ensure both clinical relevance and educational rigor. Results: The curriculum underwent 12 iterative revisions, incorporating feedback from 29 communications across AI models. Key features of the final curriculum included expanded core topics, diversified teaching methods, enhanced assessment tools, and integration into inpatient and outpatient nephrology rotations. The curriculum emphasized quality assurance, POCUS limitations, and essential clinical applications, such as fistula/graft evaluation and software integration. Alignment with certification standards further strengthened its utility. AI models contributed significantly to the curriculum's foundational structure, while human experts provided critical clinical insights. Conclusion: This curriculum, enhanced through a multiagent approach that combines AI and human collaboration, exemplifies the transformative potential of digital tools in nephrology education. The innovative framework seamlessly integrates advanced AI models with expert clinical insights, providing a scalable model for medical curriculum development that is responsive to evolving educational demands. The synergy between technological innovation and human expertise holds promising implications for advancing fellowship training. Future studies should evaluate its impact on clinical competencies and patient outcomes across diverse practice environments.

Objective Recent results from the Cardiovascular (CV) Trial of the Testosterone(T) Trials showed that T treatment of older men with low T was associated with greater progression of non-calcified plaque (NCP). We evaluated the effect of anthropometric measures and cardiovascular biomarkers on plaque progression in individuals in the T Trial. Methods The CV part of the trial included 170 men aged 65 years or older with low T. Participants received T gel or placebo gel for 12 months. The primary outcome was change in NCP volume from baseline to 12 months, as determined by coronary computed tomography angiography (CCTA). We assayed several markers of CV risk and analyzed each marker individually in a model as predictive variables and change in NCP as the dependent variable. RESULTS Of 170 enrollees, 138 (73 T, 65 placebo) completed the study and were available for the primary analysis. Of 10 markers evaluated, none showed a significant association with the change in NCP volume, but a significant interaction between treatment assignment and waist-hip ratio p-0.0014) indicated that this variable impacted the testosterone effect on non-calcified plaque volume. The statistical model indicated that for every 0.1 change in the waist-hip ratio, the T-induced 12-month change in non-calcified plaque volume increased by 26.96 mm³ (95% confidence interval 7.72, 46.20). Conclusion Among older men with low T treated for one year, greater waist-hip ratio was associated with greater NCP progression, as measured by CCTA. Other biomarkers and anthropometric measures did not show statistically significant association with plaque progression.