Comment on “Evaluating ChatGPT's Accuracy in Responding to Patient Education Questions on Acute Kidney Injury and Continuous Renal Replacement Therapy”

Blood Purification, Journal Year: 2024, Volume and Issue: 53(10), P. 847 - 848

Published: July 19, 2024

Language: Английский

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Advances in critical care nephrology through artificial intelligence

Current Opinion in Critical Care, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 2, 2024

Purpose of review This explores the transformative advancement, potential application, and impact artificial intelligence (AI), particularly machine learning (ML) large language models (LLMs), on critical care nephrology. Recent findings AI algorithms have demonstrated ability to enhance early detection, improve risk prediction, personalize treatment strategies, support clinical decision-making processes in acute kidney injury (AKI) management. ML can predict AKI up 24–48 h before changes serum creatinine levels, has identify sub-phenotypes with distinct characteristics outcomes for targeted interventions. LLMs generative offer opportunities automated note generation provide valuable patient education materials, empowering patients understand their condition options better. To fully capitalize its nephrology, it is essential confront limitations challenges implementation, including issues data quality, ethical considerations, necessity rigorous validation. Summary The integration nephrology revolutionize management continuous renal replacement therapy. While holds immense promise improving outcomes, successful implementation requires ongoing training, education, collaboration among nephrologists, intensivists, experts.

Language: Английский

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Clinical Applications and Limitations of Large Language Models in Nephrology: A Systematic Review

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Abstract Background Large Language Models (LLMs) are emerging as promising tools in healthcare. This systematic review examines LLMs’ potential applications nephrology, highlighting their benefits and limitations. Methods We conducted a literature search PubMed Web of Science, selecting studies based on Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) guidelines. The focuses the latest advancements LLMs nephrology from 2020 to 2024. PROSPERO registration number: CRD42024550169. Results Fourteen met inclusion criteria were categorized into five key areas nephrology: Streamlining workflow, disease prediction prognosis, laboratory data interpretation management, renal dietary patient education. showed high performance various clinical tasks, including managing continuous replacement therapy (CRRT) alarms (GPT-4 accuracy 90-94%) reducing intensive care unit (ICU) alarm fatigue, predicting chronic kidney diseases (CKD) progression (improved positive predictive value 6.7% 20.9%). In education, GPT-4 excelled at simplifying medical information by readability complexity, accurately translating transplant resources. Gemini provided most accurate responses frequently asked questions (FAQs) about CKD. Conclusions While incorporation shows promise across levels care, broad implementation is still premature. Further research required validate these terms accuracy, rare critical conditions, real-world performance.

Language: Английский

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Reliability and quality of information provided by artificial intelligence chatbots on post-contrast acute kidney injury: an evaluation of diagnostic, preventive, and treatment guidance

Revista da Associação Médica Brasileira, Journal Year: 2024, Volume and Issue: 70(11)

Published: Jan. 1, 2024

The aim of this study was to evaluate the reliability and quality information provided by artificial intelligence chatbots regarding diagnosis, preventive methods, treatment contrast-associated acute kidney injury, while also discussing their benefits drawbacks.

Language: Английский

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How to incorporate generative artificial intelligence in nephrology fellowship education

Journal of Nephrology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 2, 2024

Language: Английский

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