ArticleJournal of nephrology2024
How to incorporate generative artificial intelligence in nephrology fellowship education.
Article in Journal of nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Post-graduate nephrology education in China: structure, workforce gaps, regional disparities, and the emerging role of critical care nephrology.Renal failure · 2026Review
- Beyond social media: the era of generative AI and intelligent digital platforms in nephrology education.Renal failure · 2026Article
- Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology.Journal of the American Society of Nephrology : JASN · 2026Review
- Artificial Intelligence in Critical Care Nephrology: Current Applications, Emerging Techniques, and Challenges to Clinical Integration.Kidney360 · 2026Review
- Artificial intelligence in nephrology education: a multicenter survey of fellowship trainees at Mayo Clinic.Frontiers in nephrology · 2025Article
- Between the algorithm and clinical reasoning.Jornal brasileiro de nefrologiaArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional nephrology education faces challenges due to expanding medical knowledge, case complexity, and personalized learning needs. Generative artificial intelligence (AI), like ChatGPT, offers potential solutions to enhance nephrology education through dynamic, adaptive, and personalized learning experiences. We discuss integrating generative AI into nephrology education at our institution, highlighting its importance and potential applications. It explores how AI can complement traditional teaching methods by addressing challenges like information overload, diverse learning needs, and continuous learning. Generative AI models should be actively utilized under human supervision to ensure accuracy when summarizing key teaching points, creating discussion topics for journal clubs, and aiding in curriculum development for our Nephrology fellowship. Potential future applications include simulation-based learning, interactive learning modules, personalized learning plans, and enhanced research capabilities. AI can also facilitate mentorship, improve assessment, and support administrative tasks. The integration of AI addresses challenges such as keeping pace with knowledge expansion, providing personalized learning experiences, and improving access to expertise. In summary, the integration of generative AI into nephrology education represents a paradigm shift in preparing future kidney specialists. While AI offers numerous benefits, challenges such as data privacy and maintaining the human element in patient care must be addressed. A balanced approach that preserves human mentorship while employing AI's capabilities is crucial for cultivating well-rounded, competent, and compassionate nephrologists ready to tackle future kidney health challenges.
Indexed as
Identifiers
39621255What 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.