ReviewMedicines (Basel, Switzerland)2023
Exploring the Potential of Chatbots in Critical Care Nephrology.
Review in Medicines (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis 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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Telehealth in palliative care settings: A systematic review of argument-based ethics literature.Palliative medicine · 2026Pooled it
- Revolutionizing Multimorbidity Care: A Narrative Review on Artificial Intelligence Applications.Health science reports · 2026Article
- Applications of Chatbots in Improving Patient Care Outcomes:Sultan Qaboos University medical journal · 2026Article
- Exploring Patient Perspectives, Engagement, and Output Quality in Doctor-Supervised Use of Artificial Intelligence During Informed Consent Consultation With ChatGPT and Retrieval Augmented Generation (RAG): Quantitative Exploratory Study.Journal of medical Internet research · 2025Article
- Critical conversations: a user-centric approach to chatbots for history taking in the pediatric intensive care unit.Frontiers in pediatrics · 2025Article
- Testing the knowledge of artificial intelligence chatbots in pharmacology: examples of two groups of drugs.PeerJ. Computer science · 2025Article
- Ethical Considerations in Human-Centered AI: Advancing Oncology Chatbots Through Large Language Models.JMIR bioinformatics and biotechnology · 2024Article
- Behind the scenes: Key lessons learned from the RELIEVE-AKI clinical trial.Journal of critical care · 2024Review
- Needs-Assessment for an Artificial Intelligence-Based Chatbot for Pharmacists in HIV Care: Results from a Knowledge-Attitudes-Practices Survey.Healthcare (Basel, Switzerland) · 2024Article
- Chatbots in Cancer Applications, Advantages and Disadvantages: All that Glitters Is Not Gold.Journal of personalized medicine · 2024Review
- Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury.Kidney research and clinical practice · 2024Article
- Personalized Medicine Transformed: ChatGPT's Contribution to Continuous Renal Replacement Therapy Alarm Management in Intensive Care Units.Journal of personalized medicine · 2024Article
- Chain of Thought Utilization in Large Language Models and Application in Nephrology.Medicina (Kaunas, Lithuania) · 2024Review
- Large language models facilitating modern molecular biology and novel drug development.Frontiers in pharmacology · 2024Review
- 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The exponential growth of artificial intelligence (AI) has allowed for its integration into multiple sectors, including, notably, healthcare. Chatbots have emerged as a pivotal resource for improving patient outcomes and assisting healthcare practitioners through various AI-based technologies. In critical care, kidney-related conditions play a significant role in determining patient outcomes. This article examines the potential for integrating chatbots into the workflows of critical care nephrology to optimize patient care. We detail their specific applications in critical care nephrology, such as managing acute kidney injury, alert systems, and continuous renal replacement therapy (CRRT); facilitating discussions around palliative care; and bolstering collaboration within a multidisciplinary team. Chatbots have the potential to augment real-time data availability, evaluate renal health, identify potential risk factors, build predictive models, and monitor patient progress. Moreover, they provide a platform for enhancing communication and education for both patients and healthcare providers, paving the way for enriched knowledge and honed professional skills. However, it is vital to recognize the inherent challenges and limitations when using chatbots in this domain. Here, we provide an in-depth exploration of the concerns tied to chatbots' accuracy, dependability, data protection and security, transparency, potential algorithmic biases, and ethical implications in critical care nephrology. While human discernment and intervention are indispensable, especially in complex medical scenarios or intricate situations, the sustained advancements in AI signal that the integration of precision-engineered chatbot algorithms within critical care nephrology has considerable potential to elevate patient care and pivotal outcome metrics in the future.
Indexed as
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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 Socratic graph.