Benchmarking Gemini 1.5 Pro in Pediatric Emergency Radiology: Sensitivity Gap and Physiological Bias in the Immature Skeleton
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.
Authors
- Nicola Maffulli (ORCID: https://orcid.org/0000-0002-5327-3702)
- Luca Labianca (ORCID: https://orcid.org/0000-0002-0441-5110)
- Vito Caiolo
- Cristiano Benelli (ORCID: https://orcid.org/0009-0006-3493-5789)
- Gabriele Altavilla
Institutions
- Azienda Ospedaliera Sant'Andrea (IT)
Publication Details
- Journal
- Diagnostics
- Published
- 2026-09-24
- DOI
- https://doi.org/10.3390/diagnostics16193112
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00