ArticleJournal of the American Medical Informatics Association : JAMIA2024
Constructing knowledge: the role of AI in medical learning.
Article in Journal of the American Medical Informatics Association : JAMIA, 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.
- Beyond social media: the era of generative AI and intelligent digital platforms in nephrology education.Renal failure · 2026Article
- Investigating the Readability and Quality of AI Systems to Trending Questions About Food Poisoning.Journal of food science · 2026Article
- Hotspot Evolution and Future Prospects of Large Language Models in Medical Education: A Bibliometric Analysis.Advances in medical education and practice · 2026Article
- Cognitive reshaping and resurgence of humanness: restructuring the medical education continuum in the era of generative AI.Frontiers in medicine · 2026Review
- Perceptions and experiences of generative artificial intelligence training to support research for Chinese nurses: A qualitative focus group study.International journal of nursing sciences · 2025Article
- Large Language Models in Worldwide Medical Exams: Platform Development and Comprehensive Analysis.Journal of medical Internet research · 2024Article
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of large language models (LLMs) like ChatGPT into medical education presents potential benefits and challenges. These technologies, aligned with constructivist learning theories, could potentially enhance critical thinking and problem-solving through inquiry-based learning environments. However, the actual impact on educational outcomes and the effectiveness of these tools in fostering learning require further empirical study. This technological shift necessitates a reevaluation of curriculum design and the development of new assessment methodologies to measure its effects accurately. Additionally, the use of LLMs introduces significant ethical concerns, particularly in addressing inherent AI biases to ensure equitable educational access. LLMs may also help reduce global disparities in medical education by providing broader access to contemporary medical knowledge and practices, though their deployment must be managed carefully to truly support the training of competent, ethical medical professionals.
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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 OpenQuestion graph.