ArticleATS scholar2023
Large Language Models and Medical Education: Preparing for a Rapid Transformation in How Trainees Will Learn to Be Doctors.
Article in ATS scholar, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Performance and Hallucination Analysis of Large Language Models on European Anesthesiology Examinations: Cross-Sectional Comparative Study.JMIR formative research · 2026Article
- Artificial intelligence in undergraduate medical education: an updated scoping review.BMC medical education · 2025Article
- Assessing risk of bias of cohort studies with large language models.Research synthesis methods · 2025Article
- Large Language Models in Gastroenterology and Gastrointestinal Surgery: A New Frontier in Patient Communication and Education.Gastroenterology research · 2025Review
- The Role of Natural Language Processing in Graduate Medical Education: A Scoping Review.Cureus · 2025Review
- Advantages and limitations of large language models for antibiotic prescribing and antimicrobial stewardship.npj antimicrobials and resistance · 2025Review
- Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis.Journal of healthcare informatics research · 2024Article
- Assessing clinical medicine students' acceptance of large language model: based on technology acceptance model.BMC medical education · 2024Article
- In Reply to Daungsupawong and Wiwanitkit.Advances in radiation oncology · 2024Article
- CLINICAL REASONING AND ARTIFICIAL INTELLIGENCE: CAN AI REALLY THINK?Transactions of the American Clinical and Climatological Association · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence has the potential to revolutionize health care but has yet to be widely implemented. In part, this may be because, to date, we have focused on easily predicted rather than easily actionable problems. Large language models (LLMs) represent a paradigm shift in our approach to artificial intelligence because they are easily accessible and already being tested by frontline clinicians, who are rapidly identifying possible use cases. LLMs in health care have the potential to reduce clerical work, bridge gaps in patient education, and more. As we enter this era of healthcare delivery, LLMs will present both opportunities and challenges in medical education. Future models should be developed to support trainees to develop skills in clinical reasoning, encourage evidence-based medicine, and offer case-based training opportunities. LLMs may also change what we continue teaching trainees with regard to clinical documentation. Finally, trainees can help us train and develop the LLMs of the future as we consider the best ways to incorporate LLMs into medical education. Ready or not, LLMs will soon be integrated into various aspects of clinical practice, and we must work closely with students and educators to make sure these models are also built with trainees in mind to responsibly chaperone medical education into the next era.
Indexed as
Identifiers
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.