ArticleCureus2024
Exploring Generative Artificial Intelligence-Assisted Medical Education: Assessing Case-Based Learning for Medical Students.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Exploring the Application Capability of ChatGPT as an Instructor in Skills Education for Dental Medical Students: Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- Integrating ChatGPT in Orthopedic Education for Medical Undergraduates: Randomized Controlled Trial.Journal of medical Internet research · 2024Trial
- Evaluating large language models for pharmacotherapy simulations: a mixed-methods study.NPJ digital medicine · 2026Article
- Generative Artificial Intelligence-driven orthodontic education practices.BMC medical education · 2026Review
- AI in the Classroom: Observing Preclinical Students' Use of ChatGPT During Case-Based Learning at a UK Medical School.The clinical teacher · 2026Article
- Medical students perceptions and attitudes toward the use of generative artificial intelligence in clinical decision-making: a nationwide cross-sectional survey in China.BMC medical education · 2026Article
- A scoping review of the use of generative artificial intelligence tools in health profession education.BMC medical education · 2026Article
- Generative Artificial Intelligence in Health Informatics Education: A Qualitative Exploration of Student Engagement, Perceived Educational Implications, and Ethical Considerations.Advances in medical education and practice · 2026Article
- Generative AI in psychiatric education: balancing risks and benefits for responsible integration.Frontiers in psychiatry · 2026Article
- Artificial Intelligence can transform formative assessment in medical education.Canadian medical education journal · 2025Article
- Visualizing Multi-Step Decision-Making at a Glance: Pairing Choose Your Own Adventure Style Simulated Cases with a Novel Mapping Framework.Medical science educator · 2025Article
- Artificial intelligence in undergraduate medical education: an updated scoping review.BMC medical education · 2025Article
- Generative Artificial Intelligence in Medical Education: Enhancing Critical Thinking or Undermining Cognitive Autonomy?Journal of medical Internet research · 2025Article
- Generative AI in medical education: feasibility and educational value of LLM-generated clinical cases with MCQs.BMC medical education · 2025Article
- Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review.JMIR medical education · 2025Article
- Article
- AIFM-ed Curriculum Framework for Postgraduate Family Medicine Education on Artificial Intelligence: Mixed Methods Study.JMIR medical education · 2025Article
- Assessing ChatGPT 4.0's Capabilities in the United Kingdom Medical Licensing Examination (UKMLA): A Robust Categorical Analysis.Scientific reports · 2025Article
- AI-powered standardised patients: evaluating ChatGPT-4o's impact on clinical case management in intern physicians.BMC medical education · 2025Article
- Evaluation of the accuracy of large language models in answering bone cancer-related questions.Frontiers in public health · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent public release of generative artificial intelligence (GenAI) has brought fresh excitement by making access to GenAI for medical education easier than ever before. It is now incumbent upon both students and faculty to determine the optimal role of GenAI within the medical school curriculum. Given the promise and limitations of GenAI, this study aims to assess the current capabilities of a GenAI (Chat Generative Pre-trained Transformer, ChatGPT), specifically within the framework of a pre-clerkship case-based active learning curriculum. The role of GenAI is explored by evaluating its performance in generating educational materials, creating medical assessment questions, answering medical queries, and engaging in clinical reasoning by prompting it to respond to a problem-based learning scenario. Our results demonstrated that GenAI addressed epidemiology, diagnosis, and treatment questions well. However, there were still instances where it failed to provide comprehensive answers. Responses from GenAI might offer essential information, hint at the need for further inquiry, or sometimes omit critical details. GenAI struggled with generating information on complex topics, raising a significant concern when using it as a 'search engine' for medical student queries. This creates uncertainty for students regarding potentially missed critical information. With the increasing integration of GenAI into medical education, it is imperative for faculty to become well-versed in both its advantages and limitations. This awareness will enable them to educate students on using GenAI effectively in medical education.
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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.