{
  "abstract": "Generative artificial intelligence (AI) moved from an experimental novelty into mainstream use as an everyday tool in under 3 years. Large language models (LLMs) now pass portions of knowledge examinations and draft clinically relevant text that gives the impression of being comparable to human output.1–3 These tools are likely already embedded in student study habits, faculty workflows and clinical communication. Recent survey data show widespread use among learners for content review, homework assignments, draft writing and feedback, with largely insufficient institutional policies and guidance.4–7 Much literature focuses on the benefits and promises of AI models in healthcare. In this piece, we aim to highlight potential risks and provide guidance on mitigating them, with a focus on medical education.",
  "authors": [
    {
      "affiliations": [
        "School of Medicine, University of Missouri, Columbia, Missouri, USA"
      ],
      "name": "Jacob Hough"
    },
    {
      "affiliations": [
        "School of Medicine, University of Missouri, Columbia, Missouri, USA"
      ],
      "name": "Nicholas Culley"
    },
    {
      "affiliations": [
        "School of Medicine, University of Missouri, Columbia, Missouri, USA"
      ],
      "name": "Chase Erganian"
    },
    {
      "affiliations": [
        "Department of Biomedical Informatics, Biostatistics, and Medical Epidemiology, University of Missouri School of Medicine, Columbia, Missouri, USA"
      ],
      "name": "Fares Alahdab"
    }
  ],
  "title": "Potential risks of GenAI on medical education",
  "uid": "35080fc7-745a-527e-ac27-de31b7a742ec"
}