ArticleFamily medicine2024
Generative Artificial Intelligence and Large Language Models in Primary Care Medical Education.
Article in Family medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- Utility of Generative Artificial Intelligence for Japanese Medical Interview Training: Randomized Crossover Pilot Study.JMIR medical education · 2025Trial
- AI Integration in Undergraduate Medical Education: Qualitative Study of Faculty Perspectives in the United Arab Emirates.JMIR medical education · 2026Article
- Mapping the research landscape of virtual reality and artificial intelligence in medical education evaluation: A bibliometric analysis.Medicine · 2026Article
- Microlearning in medical histology: a longitudinal evaluation of faculty-created five-minute videos using five theoretical frameworks and Kirkpatrick assessment.BMC medical education · 2026Article
- How do large language models answer ADHD-related questions? A comparative study of ChatGPT, Gemini, and DeepSeek.BMC psychiatry · 2026Article
- AI integration in healthcare simulation debriefing: support or substitute?Advances in simulation (London, England) · 2026Article
- Reassessing Parente: Considering Emerging AI Pedagogies.Family medicine · 2026Article
- Postgraduate General Practice Training Under Early Clinical Responsibility: A Narrative Review on System-Based Supervision and the Supportive Role of Artificial Intelligence.Healthcare (Basel, Switzerland) · 2026Review
- Validation of the Use of a Large Language Model for Detecting Sentiment in Student Course Evaluation.Family medicine · 2026Article
- University students' acceptance of generative artificial intelligence tools: a mixed-methods study on opinions, attitudes, and behavioral intentions.BMC psychology · 2026Article
- Effect of Emotional Prompt on the Quality of ChatGPT Based Discharge Instructions After Laparoscopic Cholecystectomy Using ERAS Derived Benchmarks.Journal of multidisciplinary healthcare · 2026Article
- Virtual patient simulation to enhance medical students' clinical communication and decision-making skills: a pilot study.BMC medical education · 2025Article
- Comparing ChatGPT-3.5, Gemini 2.0, and DeepSeek V3 for pediatric pneumonia learning in medical students.Scientific reports · 2025Article
- Digital Learning Tools in Postgraduate Family Medicine Training: A Scoping Review.Family medicine · 2025Article
- Artificial Intelligence in Nutrition and Dietetics: A Comprehensive Review of Current Research.Healthcare (Basel, Switzerland) · 2025Review
- Article
- Artificial Intelligence in Graduate Medical Education.Annals of family medicine · 2025Article
- Performance Evaluation of 18 Generative AI Models (ChatGPT, Gemini, Claude, and Perplexity) in 2024 Japanese Pharmacist Licensing Examination: Comparative Study.JMIR medical education · 2025Article
- Development of a Clinical Clerkship Mentor Using Generative AI and Evaluation of Its Effectiveness in a Medical Student Trial Compared to Student Mentors: 2-Part Comparative Study.JMIR medical education · 2025Article
- Use and Evaluation of Generative Artificial Intelligence by Medical Students in Japan.JMA journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence and large language models are the continuation of a technological revolution in information processing that began with the invention of the transistor in 1947. These technologies, driven by transformer architectures for artificial neural networks, are poised to broadly influence society. It is already apparent that these technologies will be adapted to drive innovation in education. Medical education is a high-risk activity: Information that is incorrectly taught to a student may go unrecognized for years until a relevant clinical situation appears in which that error can lead to patient harm. In this article, I discuss the principal limitations to the use of generative artificial intelligence in medical education-hallucination, bias, cost, and security-and suggest some approaches to confronting these problems. Additionally, I identify the potential applications of generative artificial intelligence to medical education, including personalized instruction, simulation, feedback, evaluation, augmentation of qualitative research, and performance of critical assessment of the existing scientific literature.
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.