{
  "abstract": "Pain assessment remains challenging in clinical practice. Automated pain assessment using artificial intelligence (AI) and facial expression analysis has emerged as a potential approach to support more objective and continuous pain monitoring, particularly for patients who are unable to communicate effectively. However, successful implementation depends on clinician acceptance. We conducted a cross-sectional survey of physicians, advanced practice providers (APPs), and nurses at a tertiary academic medical center to evaluate current pain assessment practices and perceptions of a facial-analysis-based pain assessment system. Eighty-seven eligible clinicians participated. For current assessment practices, clinicians commonly relied on observational indicators of pain, including vital signs (88.5%), physical examination findings (85.1%), and facial expressions (79.3%). Attitudes toward automated pain assessment were generally neutral to positive. Half of respondents agreed that such a system would be useful for monitoring patients over time (50.6%), and many perceived value for nonverbal patients (86.5%), patients with dementia or cognitive impairment (78.4%), and patients with language barriers (62.2%). Most clinicians reported requiring high levels of accuracy before routine use, with nearly 60% requiring at least 90% accuracy. Key concerns included alert fatigue and unequal performance across patient populations. These findings suggest clinicians view automated facial-analysis systems as potentially valuable adjuncts for pain monitoring, particularly in communication-impaired populations, while emphasizing the importance of accuracy, fairness, explainability, and workflow integration during system development and implementation.",
  "abstract_shown": true,
  "affiliations": [
    "University of Virginia School of Medicine"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "C. S. Mullins"
    },
    {
      "name": "C. Espelien"
    },
    {
      "name": "M. Gurka"
    },
    {
      "name": "P. Chernyavskiy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4pttk2e55mvq6.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.21.26363597",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.21.26363597v1.full",
  "id": "4pttk2e55mvq6",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.21.26363597v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "pain medicine"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7214122612",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.21.26363597v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4pttk2e55mvq6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:12Z",
      "native_id": "10.64898/2026.09.21.26363597",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.21.26363597v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:pain medicine",
      "subfield": "surgery"
    }
  ],
  "title": "Clinician Perspectives on Automated Facial Analysis for Pain Assessment: A Cross-Sectional Survey of Physicians, Advanced Practice Providers, and Nurses",
  "title_text": "Clinician Perspectives on Automated Facial Analysis for Pain Assessment: A Cross-Sectional Survey of Physicians, Advanced Practice Providers, and Nurses",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:12Z",
  "url": "https://research.draconitic.com/paper/4pttk2e55mvq6",
  "venue": "medRxiv",
  "versions": []
}