ArticleAnnals of medicine and surgery (2012)2023
ChatGPT and artificial hallucinations in stem cell research: assessing the accuracy of generated references - a preliminary study.
Article in Annals of medicine and surgery (2012), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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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
11 citing papers in PubMed.
- Comparison of the performance of large language models in answering patient questions related to cataract.iScience · 2026Article
- Reference Accuracy in Large Language Model Chatbots: A Metric for Inherent Misinformation?Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Magnitude and Impact of Hallucinations in Tabular Synthetic Health Data on Prognostic Machine Learning Models: Validation Study.Journal of medical Internet research · 2025Article
- How generative artificial intelligence portrays science: Interviewing ChatGPT from the perspective of different audience segments.Public understanding of science (Bristol, England) · 2025Article
- Beyond the Scalpel: Assessing ChatGPT's potential as an auxiliary intelligent virtual assistant in oral surgery.Computational and structural biotechnology journal · 2024Article
- Article
- RefAI: a GPT-powered retrieval-augmented generative tool for biomedical literature recommendation and summarization.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective.Life (Basel, Switzerland) · 2024Article
- ChatGPT provides inconsistent risk-stratification of patients with atraumatic chest pain.PloS one · 2024Article
- Medical validity and layperson interpretation of emergency visit recommendations by the GPT model: A cross-sectional study.Acute medicine & surgeryArticle
- ChatGPT-4o Mini Fabricates and Miscites Evidence for American Academy of Orthopaedic Surgeons Hip Fracture Clinical Practice Guidelines.JB & JS open accessArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stem cell research has the transformative potential to revolutionize medicine. Language models like ChatGPT, which use artificial intelligence (AI) and natural language processing, generate human-like text that can aid researchers. However, it is vital to ensure the accuracy and reliability of AI-generated references. This study assesses Chat Generative Pre-Trained Transformer (ChatGPT)'s utility in stem cell research and evaluates the accuracy of its references. Of the 86 references analyzed, 15.12% were fabricated and 9.30% were erroneous. These errors were due to limitations such as no real-time internet access and reliance on preexisting data. Artificial hallucinations were also observed, where the text seems plausible but deviates from fact. Monitoring, diverse training, and expanding knowledge cut-off can help to reduce fabricated references and hallucinations. Researchers must verify references and consider the limitations of AI models. Further research is needed to enhance the accuracy of such language models. Despite these challenges, ChatGPT has the potential to be a valuable tool for stem cell research. It can help researchers to stay up-to-date on the latest developments in the field and to find relevant information.
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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.