ArticleJMIR formative research2025
Assessing the Current Limitations of Large Language Models in Advancing Health Care Education.
Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
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Who cites it
19 citing papers in PubMed.
- Artificial intelligence advancements for orthopaedic clinical reasoning: longitudinal assessment of newer models (ChatGPT-5, Grok-3, Gemini 2.5 Flash) compared to clinicians.Archives of orthopaedic and trauma surgery · 2026Article
- Artificial Intelligence Performance Under Different Conditions in Answering China's Standardized Training Examination for Resident Physician in Radiology: A Comparative Analysis.Health care science · 2026Article
- Blinded by the Bot: Benchmarking GPT and Gemini Against Human Authors in Otolaryngology Reviews.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- A systematic review of the limitations of large language models in generating healthcare content.PLOS digital health · 2026Article
- Applications of Large Language Models in Glaucoma: A Scoping Review.Vision (Basel, Switzerland) · 2026Review
- From dictation to diagnosis: enhancing radiology reporting with integrated speech recognition in multimodal large language models.European radiology · 2026Article
- Comparison of reference management software with new artificial intelligence-based tools.Journal of educational evaluation for health professions · 2026Review
- Performance evaluation of mainstream large language models in autoimmune hepatitis patient education: a comparative study of readability, quality, and reliability.Frontiers in public health · 2026Article
- The fragile intelligence of GPT-5 in medicine.Nature medicine · 2025Article
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- ChatGPT in Nursing: Applications, Advantages, and Challenges in Education, Research, and Clinical Practice.Annals of biomedical engineering · 2025Review
- Public Health Risk Management, Policy, and Ethical Imperatives in the Use of AI Tools for Mental Health Therapy.Healthcare (Basel, Switzerland) · 2025Article
- Assessing the Role of Large Language Models Between ChatGPT and DeepSeek in Asthma Education for Bilingual Individuals: Comparative Study.JMIR medical informatics · 2025Article
- Assessment of Recommendations Provided to Athletes Regarding Sleep Education by GPT-4o and Google Gemini: Comparative Evaluation Study.JMIR formative research · 2025Article
- Large Language Model-Assisted Surgical Consent Forms in Non-English Language: Content Analysis and Readability Evaluation.Journal of medical Internet research · 2025Article
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- Large language models in clinical nutrition: an overview of its applications, capabilities, limitations, and potential future prospects.Frontiers in nutrition · 2025Review
- A 25-year retrospective of Canadian plastic surgery research and its influence: a bibliometric study.Canadian journal of surgery. Journal canadien de chirurgieArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unlabelled: The integration of large language models (LLMs), as seen with the generative pretrained transformers series, into health care education and clinical management represents a transformative potential. The practical use of current LLMs in health care sparks great anticipation for new avenues, yet its embracement also elicits considerable concerns that necessitate careful deliberation. This study aims to evaluate the application of state-of-the-art LLMs in health care education, highlighting the following shortcomings as areas requiring significant and urgent improvements: (1) threats to academic integrity, (2) dissemination of misinformation and risks of automation bias, (3) challenges with information completeness and consistency, (4) inequity of access, (5) risks of algorithmic bias, (6) exhibition of moral instability, (7) technological limitations in plugin tools, and (8) lack of regulatory oversight in addressing legal and ethical challenges. Future research should focus on strategically addressing the persistent challenges of LLMs highlighted in this paper, opening the door for effective measures that can improve their application in health care 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.