Purpose: ChatGPT is a large language model trained on a large dataset covering a broad range of topics, including the medical literature. We aim to examine its accuracy and reproducibility in answering patient questions regarding bariatric surgery. Materials and methods: Questions were gathered from nationally regarded professional societies and health institutions as well as Facebook support groups. Board-certified bariatric surgeons graded the accuracy and reproducibility of responses. The grading scale included the following: (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, and (4) completely incorrect. Reproducibility was determined by asking the model each question twice and examining difference in grading category between the two responses. Results: In total, 151 questions related to bariatric surgery were included. The model provided “comprehensive” responses to 131/151 (86.8%) of questions. When examined by category, the model provided “comprehensive” responses to 93.8% of questions related to “efficacy, eligibility and procedure options”; 93.3% related to “preoperative preparation”; 85.3% related to “recovery, risks, and complications”; 88.2% related to “lifestyle changes”; and 66.7% related to “other”. The model provided reproducible answers to 137 (90.7%) of questions. Conclusion: The large language model ChatGPT often provided accurate and reproducible responses to common questions related to bariatric surgery. ChatGPT may serve as a helpful adjunct information resource for patients regarding bariatric surgery in addition to standard of care provided by licensed healthcare professionals. We encourage future studies to examine how to leverage this disruptive technology to improve patient outcomes and quality of life. Graphical

Heart failure requires complex management, and increased patient knowledge has been shown to improve outcomes. This study assessed the knowledge of Chat Generative Pre-trained Transformer (ChatGPT) and its appropriateness as a supplemental resource of information for patients with heart failure. A total of 107 frequently asked heart failure-related questions were included in 3 categories: "basic knowledge" (49), "management" (41) and "other" (17). Two responses per question were generated using both GPT-3.5 and GPT-4 (i.e., two responses per question per model). The accuracy and reproducibility of responses were graded by two reviewers, board-certified in cardiology, with differences resolved by a third reviewer, board-certified in cardiology and advanced heart failure. Accuracy was graded using a four-point scale: (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, and (4) completely incorrect. GPT-4 provided 107/107 (100%) responses with correct information. Further, GPT-4 displayed a greater proportion of comprehensive knowledge for the categories of "basic knowledge" and "management" (89.8% and 82.9%, respectively). For GPT-3, there were two total responses (1.9%) graded as "some correct and incorrect" for GPT-3.5, while no "completely incorrect" responses were produced. With respect to comprehensive knowledge, GPT-3.5 performed best in the "management" category and "other" category (prognosis, procedures, and support) (78.1%, 94.1%). The models also provided highly reproducible responses, with GPT-3.5 scoring above 94% in every category and GPT-4 with 100% for all answers. GPT-3.5 and GPT-4 answered the majority of heart failure-related questions accurately and reliably. If validated in future studies, ChatGPT may serve as a useful tool in the future by providing accessible health-related information and education to patients living with heart failure. In its current state, ChatGPT necessitates further rigorous testing and validation to ensure patient safety and equity across all patient demographics.

Background and aims: Pancreatic cancer (PC) incidence is increasing at a greater rate in young women compared to young men. We performed a race- and ethnicity-specific evaluation of incidence trends in subgroups stratified by age and sex to investigate the association of race and ethnicity with these trends. Methods: Age-adjusted PC incidence rates (IR) from the years 2000 to 2018 were obtained from the SEER 21 database. Non-Hispanic White (White), Non-Hispanic Black (Black) and Hispanic patients were included. Age categories included older (ages ≥ 55) and younger (ages < 55) adults. Time-trends were described as annual percentage change (APC) and average APC (AAPC). Results: Younger White [AAPC difference = 0.73, p = 0.01)], Black [AAPC difference = 1.96, p = 0.01)] and Hispanic [AAPC difference = 1.55, p = 0.011)] women experienced a greater rate of increase in IR compared to their counterpart men. Younger Hispanic women experienced a greater rate of increase in IR compared to younger Black women [AAPC difference = −1.28, p = 0.028)] and younger White women [AAPC difference = −1.35, p = 0.011)]. Conclusion: Younger women of all races and ethnicities experienced a greater rate of increase in PC IR compared to their counterpart men; however, younger Hispanic and Black women experienced a disproportionately greater increase. Hispanic women experienced a greater rate of increase in IR compared to younger Black and White women.