{
  "abstract": "Computer vision approaches to disease motor assessment are limited by clinical video dataset scarcity and distributional imbalance, motivating interest in generative video models as a potential source of training data. We introduce a three-component framework for evaluating the clinical fidelity of synthetic medical motion data, assessing visual fidelity, biomechanical stability, and pathophysiological accuracy, and apply it to videos generated by the text-to-video model Sora 2 depicting Parkinsonian hand-fisting motions across MDS-UPDRS severity levels 0-2 (normal to mild-moderate motor impairment). We further use real-versus-real bootstrapping to compare synthetic-real differences against the natural variability of real clinical videos. Although generated videos appear visually realistic, quantitative analysis shows substantial divergence from real clinical recordings, including weaker severity-dependent patterns of bradykinesia and hypokinesia, less realistic tremor rhythms, and substantial temporal instability. These findings reveal a motion-domain gap between Sora-2-generated and real clinical motor behavior in this Parkinsonian hand-motion setting and demonstrate that visual realism alone is insufficient to establish the clinical utility of synthetic video data for training or evaluating medical computer vision systems.",
  "abstract_shown": true,
  "affiliations": [
    "University of Pennsylvania"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Y. Feng"
    },
    {
      "name": "S. Mopidevi"
    },
    {
      "name": "K. Kinfu"
    },
    {
      "name": "J. Park"
    },
    {
      "name": "R. Vidal"
    },
    {
      "affs": [
        0
      ],
      "name": "K. B. Johnson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4txw69asrvadh.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.05.26363719",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.05.26363719v1.full",
  "id": "4txw69asrvadh",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.05.26363719v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7220924735",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.05.26363719v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4txw69asrvadh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:22Z",
      "native_id": "10.64898/2026.10.05.26363719",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.05.26363719v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Can Generative Video Address the Clinical Video Data Gap? Evaluation of Synthetic Parkinsonian Hand Motions",
  "title_text": "Can Generative Video Address the Clinical Video Data Gap? Evaluation of Synthetic Parkinsonian Hand Motions",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:22Z",
  "url": "https://research.draconitic.com/paper/4txw69asrvadh",
  "venue": "medRxiv",
  "versions": []
}