ReviewHealthcare (Basel, Switzerland)2023
Enhancing Kidney Transplant Care through the Integration of Chatbot.
Review in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Leveraging chatbots for enhanced decision-making: a comprehensive literature review.Frontiers in artificial intelligence · 2026Pooled it
- Perioperative application of chatbots: a systematic review and meta-analysis.BMJ health & care informatics · 2024Pooled it
- Evaluation of the applicability of AI chatbots in addressing health management issues for living kidney donors.International urology and nephrology · 2026Article
- Early Implications for Solid Organ Transplantation With the Use of Artificial Intelligence From a Bibliometric Perspective.Mayo Clinic proceedings. Digital health · 2026Review
- AI-Assisted Decision-Making for End-Stage Organ Failure: Opportunities and Ethical Concerns.Artificial organs · 2026Review
- AI-generated explanations in kidney transplantation: accuracy vs. readability and implications for patient education.Frontiers in artificial intelligence · 2026Article
- Quality of Chatbot Responses to the Most Popular Questions Regarding Erectile Dysfunction.Urology research & practice · 2025Article
- Research progress and implications of the application of large language model in shared decision-making in China's healthcare field.Frontiers in public health · 2025Review
- Performance of o1 pro and GPT-4 in Self-Assessment Questions for Nephrology Board Renewal.Frontiers in medicine · 2025Article
- Immunocompatibility in transplantation: adapting to a changing therapeutic landscape.Frontiers in immunology · 2025Review
- Advancing health equity: evaluating AI translations of kidney donor information for Spanish speakers.Frontiers in public health · 2025Article
- Perspectives on AI-based recommendations for mask-wearing and COVID-19 vaccination for transplant recipients in the post-COVID-19 era.Renal failure · 2024Article
- Navigating Nephrology's Decline Through a GPT-4 Analysis of Internal Medicine Specialties in the United States: Qualitative Study.JMIR medical education · 2024Article
- Evolution of Chatbots in Nursing Education: Narrative Review.JMIR medical education · 2024Review
- AI and Ethics: A Systematic Review of the Ethical Considerations of Large Language Model Use in Surgery Research.Healthcare (Basel, Switzerland) · 2024Review
- AI-driven translations for kidney transplant equity in Hispanic populations.Scientific reports · 2024Article
- Exploring the Role of ChatGPT in Cardiology: A Systematic Review of the Current Literature.Cureus · 2024Review
- Chain of Thought Utilization in Large Language Models and Application in Nephrology.Medicina (Kaunas, Lithuania) · 2024Review
- Empowering inclusivity: improving readability of living kidney donation information with ChatGPT.Frontiers in digital health · 2024Article
- Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review.Clinics and practice · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Kidney transplantation is a critical treatment option for end-stage kidney disease patients, offering improved quality of life and increased survival rates. However, the complexities of kidney transplant care necessitate continuous advancements in decision making, patient communication, and operational efficiency. This article explores the potential integration of a sophisticated chatbot, an AI-powered conversational agent, to enhance kidney transplant practice and potentially improve patient outcomes. Chatbots and generative AI have shown promising applications in various domains, including healthcare, by simulating human-like interactions and generating contextually appropriate responses. Noteworthy AI models like ChatGPT by OpenAI, BingChat by Microsoft, and Bard AI by Google exhibit significant potential in supporting evidence-based research and healthcare decision making. The integration of chatbots in kidney transplant care may offer transformative possibilities. As a clinical decision support tool, it could provide healthcare professionals with real-time access to medical literature and guidelines, potentially enabling informed decision making and improved knowledge dissemination. Additionally, the chatbot has the potential to facilitate patient education by offering personalized and understandable information, addressing queries, and providing guidance on post-transplant care. Furthermore, under clinician or transplant pharmacist supervision, it has the potential to support post-transplant care and medication management by analyzing patient data, which may lead to tailored recommendations on dosages, monitoring schedules, and potential drug interactions. However, to fully ascertain its effectiveness and safety in these roles, further studies and validation are required. Its integration with existing clinical decision support systems may enhance risk stratification and treatment planning, contributing to more informed and efficient decision making in kidney transplant care. Given the importance of ethical considerations and bias mitigation in AI integration, future studies may evaluate long-term patient outcomes, cost-effectiveness, user experience, and the generalizability of chatbot recommendations. By addressing these factors and potentially leveraging AI capabilities, the integration of chatbots in kidney transplant care holds promise for potentially improving patient outcomes, enhancing decision making, and fostering the equitable and responsible use of AI in healthcare.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.