{
  "abstract": "Background Artificial intelligence (AI)-generated text to video is emerging in medical education, but its effectiveness, accuracy and safety remain uncertain. We aimed to synthesise empirical studies evaluating these tools in learner or patient education.Methods A comprehensive search was conducted in MEDLINE/PubMed, Google Scholar, Scopus, Cochrane Review and Web of Science for studies published up to January 2025. Eligible studies evaluated AI-generated text to video for medical or patient education, reporting both quantitative and qualitative outcomes. Two reviewers screened and extracted data. The review adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.Results Out of 103 identified studies, five studies met the inclusion criteria: four evaluated patient education and one evaluated physician training. Clinical areas were ophthalmology (2/5), plastic surgery (1/5), dysphagia rehabilitation (1/5) and neurosurgical training (1/5). In ophthalmology, control materials outperformed AI-generated videos on image/script accuracy (p<0.005), with similar script-image alignment. In dysphagia rehabilitation, a randomised trial reported improvements in swallowing function and related outcomes with an AI-assisted video game intervention (p<0.001). A plastic surgery study reported greater user preference for a video avatar tool compared with a text chatbot (63.5% vs 28.1%). Across the reviewed studies, samples were small and CIs were rarely reported. Outcome measures were heterogeneous, and meta-analysis was not feasible.Conclusion AI-generated videos can enhance engagement or selected outcomes in certain contexts, yet concerns about accuracy and inconsistent measurement persist. Current evidence is sparse and mixed. Currently, these tools can complement, rather than replace, standard resources until non-inferiority is demonstrated for the prespecified primary outcomes.PROSPERO registration number CRD42025640042.",
  "authors": [
    {
      "affiliations": [
        "Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel"
      ],
      "name": "Yaara Artsi"
    },
    {
      "affiliations": [
        "Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA"
      ],
      "name": "Vera Sorin"
    },
    {
      "affiliations": [
        "Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA",
        "The Windreich Department of Artificial Intelligence and Human Health, Mount Sinai Medical Center, New York, New York, USA"
      ],
      "name": "Benjamin Glicksberg"
    },
    {
      "affiliations": [
        "Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA"
      ],
      "name": "Panagiotis Korfiatis"
    },
    {
      "affiliations": [
        "Medical Education and Rehabilitation and Human Performance, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA"
      ],
      "name": "David C Thomas"
    },
    {
      "affiliations": [
        "The Windreich Department of Artificial Intelligence and Human Health, Mount Sinai Medical Center, New York, New York, USA",
        "Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA",
        "The Hasso Plattner Institute of Digital Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA"
      ],
      "name": "Girish N Nadkarni"
    },
    {
      "affiliations": [
        "The Windreich Department of Artificial Intelligence and Human Health, Mount Sinai Medical Center, New York, New York, USA",
        "Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA",
        "The Hasso Plattner Institute of Digital Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA"
      ],
      "name": "Eyal Klang"
    }
  ],
  "title": "AI-generated videos in medical education: systematic review",
  "uid": "a85ddd06-0c37-5d70-b0e3-9c6c50a46962"
}