{
  "abstract": "ObjectivesArtificial intelligence (AI) architectures demonstrate high proficiency in chest radiography interpretation, yet successful clinical integration is often hampered by usability challenges and physician skepticism. This study evaluates the ergonomic usability and professional acceptance of the MeDeep AI platform among medical practitioners to identify key drivers of clinical trust.\n\nMethodsA multicenter, cross-sectional study was conducted across hospitals in Kermanshah, evaluating 88 medical professionals, including attending radiologists, radiology residents, internal medicine physicians, and medical externs. The System Usability Scale (SUS) was utilized to measure the platforms ergonomic functionality (n = 82), while the validated ATRAI-14 questionnaire quantified broader attitudes and trust regarding AI integration (n = 88).\n\nResultsThe MeDeep platform achieved a high mean SUS score of 84.93, indicating excellent usability uniformly experienced across all professional subgroups (p = 0.387). However, overarching AI acceptance and algorithmic trust varied significantly by clinical role. Radiology residents exhibited significantly higher overarching AI acceptance (p = 0.016) and algorithmic trust (p = 0.008) compared to attending radiologists. Multivariate regression demonstrated that user familiarity (Beta = 0.500, p < 0.001) and perceived system usability (Beta = 0.236, p = 0.012)--rather than years of clinical experience--were the primary independent positive predictors of AI acceptance. Furthermore, higher perceived usability directly predicted greater algorithmic trust (Beta = 0.282, p = 0.009).\n\nConclusionWhile technical usability is a necessary baseline for diagnostic platforms, professional trust dictates ultimate clinical adoption. Overcoming the generational divide in AI acceptance requires developers to prioritize intuitive interface design and position AI as a workflow-augmenting tool that addresses administrative burdens.\n\nKey PointsO_LIThe MeDeep AI platform demonstrated excellent ergonomic usability uniformly across all professional clinical ranks. C_LIO_LISignificant generational divides exist, with residents exhibiting higher algorithmic trust than attending radiologists. C_LIO_LIUser familiarity and perceived usability predict overarching AI acceptance better than years of clinical experience. C_LI",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "P. Shariat Rad"
    },
    {
      "name": "M. M. Hatami"
    },
    {
      "name": "M. A. Amani"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Salehi"
    },
    {
      "name": "M. H. Sharif"
    },
    {
      "name": "M. J. Gharegozlou"
    },
    {
      "affs": [
        0
      ],
      "name": "M. G. Salehi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2degbk40q8dk6.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.23.26365338",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.23.26365338v1.full",
  "id": "2degbk40q8dk6",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.23.26365338v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "radiology and imaging"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7223698307",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.23.26365338v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2degbk40q8dk6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:50Z",
      "native_id": "10.64898/2026.10.23.26365338",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.23.26365338v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Usability and Physician Acceptance of AI in Chest Radiography: An Evaluation of the MeDeep Platform Using SUS and ATRAI-14",
  "title_text": "Usability and Physician Acceptance of AI in Chest Radiography: An Evaluation of the MeDeep Platform Using SUS and ATRAI-14",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:50Z",
  "url": "https://research.draconitic.com/paper/2degbk40q8dk6",
  "venue": "medRxiv",
  "versions": []
}