{
  "abstract": "Background/Objectives: To assess the diagnostic performance of Gemini 1.5 Pro, a general-purpose Large Multimodal Model (LMM), as a zero-shot, smartphone-based decision support tool to distinguish pediatric fractures from healthy controls. Methods: A retrospective diagnostic accuracy study was conducted on 182 pediatric radiographs (112 confirmed fractures, 70 healthy controls) using the Gemini 1.5 Pro mobile interface with a 4-question prompt protocol (Detection, Characterization, Skeletal Maturity, Management). Diagnostic metrics were calculated with 95% Confidence Intervals (95% CI). Ground truth was established by a senior musculoskeletal radiologist. Results: The model showed an overall sensitivity of 90.2% (95% CI: 83.1-95.0%) and specificity of 87.1% (95% CI: 77.0-93.9%). A significant sensitivity gap occurred between displaced (100.0%; 95% CI: 91.4-100.0%) and non-displaced fractures (84.5%; 95% CI: 74.0-92.0%; p = 0.007). Qualitative taxonomy revealed a primary physiological bias leading to a 12.8% false-positive rate due to growth plate misinterpretation, alongside minor characterization and localization errors (3.6% of correctly identified fractures). When fractures were correctly identified, proposed management strategies aligned with international standards in 95.0% of cases. Inter-observer reliability was excellent (weighted k = 0.96). Conclusions: Gemini 1.5 Pro displays robust clinical reasoning for obvious trauma. However, its zero-shot performance is limited by subtle morphological disruptions and developmental anatomical nuances such as open growth plates. It shows promise as a mobile clinical decision support system for non-specialists, but expert human supervision remains necessary to ensure safety in the complex radiographic landscape of the immature skeleton.",
  "abstract_shown": true,
  "affiliations": [
    "Orthopedic Unit, Sant'Andrea Hospital, University of Rome La Sapienza, Via di Grottarossa 1035, 00189 Rome, Italy."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Cristiano Benelli"
    },
    {
      "affs": [
        0
      ],
      "name": "Luca Labianca"
    },
    {
      "affs": [
        0
      ],
      "name": "Vito Caiolo"
    },
    {
      "affs": [
        0
      ],
      "name": "Gabriele Altavilla"
    },
    {
      "affs": [
        0
      ],
      "name": "Nicola Maffulli",
      "orcid": "0000-0002-5327-3702"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8ndxj36zkh26b.bib",
  "cited_by": 0,
  "doi": "10.3390/diagnostics16193112",
  "fulltext_url": "https://europepmc.org/articles/PMC13664553",
  "id": "8ndxj36zkh26b",
  "issns": [
    "2075-4418"
  ],
  "landing_url": "https://europepmc.org/article/MED/42882268",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7214234590",
  "pdf_url": "https://europepmc.org/articles/PMC13664553?pdf=render",
  "pmcid": "PMC13664553",
  "pmid": "42882268",
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8ndxj36zkh26b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:16Z",
      "native_id": "MED:42882268",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42882268"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Benchmarking Gemini 1.5 Pro in Pediatric Emergency Radiology: Sensitivity Gap and Physiological Bias in the Immature Skeleton.",
  "title_text": "Benchmarking Gemini 1.5 Pro in Pediatric Emergency Radiology: Sensitivity Gap and Physiological Bias in the Immature Skeleton.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:16Z",
  "url": "https://research.draconitic.com/paper/8ndxj36zkh26b",
  "venue": "Diagnostics (Basel, Switzerland)",
  "versions": []
}