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Benchmarking Open-Source Vision Language Models in Orthopedic In-Training Examination: A Comparison with Residents,

Sunho Ko1, Jaewook Lee2, Kyunga Ko3

  • 1Department of Orthopedic Surgery, Seoul National University Hospital, Seoul, Korea.

Clinics in Orthopedic Surgery
|February 6, 2026
PubMed
Summary
This summary is machine-generated.

Open-source vision-language models (VLMs) show promise in orthopedic care, with large models matching resident performance. However, smaller models and specific subspecialties like spine and pediatrics require further development.

Keywords:
Artificial intelligenceLarge language modelOrthopedic in-training examinationVision language model

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Orthopedic Surgery Education

Background:

  • Advancing orthopedic care necessitates multimodal AI, including image processing and secure deployment.
  • Current literature underexplores open-source vision-language models (VLMs) for these applications.
  • Secure in-house operations and multimodal capabilities are critical for AI integration in orthopedics.

Purpose of the Study:

  • Benchmark open-source VLMs against orthopedic residents using the Orthopedic In-Training Examination (OITE).
  • Assess VLM performance across orthopedic subspecialties.
  • Investigate the relationship between VLM parameter size and performance.

Main Methods:

  • Evaluated six open-source VLMs of varying sizes using the 2023 OITE (210 questions, 111 with images).
  • Compared model performance against 2023 OITE resident scores.
  • Utilized Pearson correlation to analyze the association between model size and performance.

Main Results:

  • The largest VLMs (Qwen2.5-VL-72B, Llama-3.2-90B) performed comparably to second-year residents.
  • A mid-sized model (Qwen-32B) outperformed first-year residents; smaller models performed below first-year residents.
  • Qwen2.5-VL-72B excelled in foot/ankle and sports medicine; Llama-3.2-90B led in basic science and hand/wrist.
  • All models struggled with spine and pediatric questions; accuracy increased with size up to 72B parameters.

Conclusions:

  • Smaller VLMs offer lower hardware needs but reduced accuracy.
  • Spine and pediatric domains remain challenging for current VLMs.
  • Model selection requires balancing clinical needs with hardware limitations based on domain-specific benchmarks.
  • Open-source VLMs show potential but need further refinement for clinical and educational use.