Citations

Chronic kidney disease (CKD) patients can benefit from personalized education on lifestyle and nutrition management strategies to enhance healthcare outcomes. The potential use of chatbots, introduced in 2022, as a tool for educating CKD patients has been explored. A set of 15 questions on lifestyle modification and nutrition, derived from a thorough review of three specific KDIGO guidelines, were developed and posed in various formats, including original, paraphrased with different adverbs, incomplete sentences, and misspellings. Four versions of AI were used to answer these questions: ChatGPT 3.5 (March and September 2023 versions), ChatGPT 4, and Bard AI. Additionally, 20 questions on lifestyle modification and nutrition were derived from the NKF KDOQI guidelines for nutrition in CKD (2020 Update) and answered by four versions of chatbots. Nephrologists reviewed all answers for accuracy. ChatGPT 3.5 produced largely accurate responses across the different question complexities, with occasional misleading statements from the March version. The September 2023 version frequently cited its last update as September 2021 and did not provide specific references, while the November 2023 version did not provide any misleading information. ChatGPT 4 presented answers similar to 3.5 but with improved reference citations, though not always directly relevant. Bard AI, while largely accurate with pictorial representation at times, occasionally produced misleading statements and had inconsistent reference quality, although an improvement was noted over time. Bing AI from November 2023 had short answers without detailed elaboration and sometimes just answered “YES”. Chatbots demonstrate potential as personalized educational tools for CKD that utilize layman’s terms, deliver timely and rapid responses in multiple languages, and offer a conversational pattern advantageous for patient engagement. Despite improvements observed from March to November 2023, some answers remained potentially misleading. ChatGPT 4 offers some advantages over 3.5, although the differences are limited. Collaboration between healthcare professionals and AI developers is essential to improve healthcare delivery and ensure the safe incorporation of chatbots into patient care.

Background. Normothermic regional perfusion (NRP) has emerged as a vital technique in organ procurement, particularly in donation after circulatory death (DCD) cases, offering the potential to optimize organ utilization and improve posttransplant outcomes. Recognizing its significance, the American Society of Transplant Surgeons (ASTS) convened a work group to develop standardized recommendations for abdominal NRP in the United States. Methods. The workgroup, comprising experts in NRP, DCD, and transplantation, formulated recommendations through a collaborative process involving revisions and approvals by relevant committees and the ASTS council. Four key areas were identified for standardization: Preprocedure communication, NRP procedure, Terminology and documentation, and Mentorship/credentialing. Results. The recommendations encompass a range of considerations, including preprocedure communication protocols to facilitate informed decision-making by transplant centers and organ procurement organizations, procedural guidelines for NRP teams, uniform terminology to clarify the NRP process, and standards for mentorship and credentialing of NRP practitioners. Specific recommendations address logistical concerns, procedural nuances, documentation requirements, and the importance of ongoing quality assurance. Conclusions. The standardized recommendations for abdominal NRP presented in this article aim to ensure consistency, safety, and efficacy in the organ procurement process. By establishing clear protocols and guidelines, the ASTS seeks to enhance organ utilization, honor donor wishes, and uphold public trust in the donation process. Implementation of these recommendations can contribute to the advancement of NRP practices and improve outcomes for transplant recipients.

Abstract Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of Sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32‐item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit GenAIMed.org .