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Large language models with image processing in automated Cobb angle.

Jonathan Gibson1, Sanika Kharwadkar2, Chuck Lam1

  • 1College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.

European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
|February 20, 2026
PubMed
Summary
This summary is machine-generated.

Current large language models (LLMs) demonstrate limited accuracy in measuring Cobb angles for scoliosis assessment. AI tools require further development before clinical integration for spinal deformity analysis.

Keywords:
Artificial intelligence, scoliosis, Cobb angle, large language models, technology

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Spinal deformity assessment

Background:

  • Scoliosis severity is quantified by the Cobb angle measured on radiographs.
  • The integration of artificial intelligence (AI) into clinical workflows raises questions about its utility in spinal deformity classification.
  • Evaluating the capabilities of leading large language models (LLMs) in clinical applications is crucial.

Purpose of the Study:

  • To assess the diagnostic accuracy of four prominent LLMs (ChatGPT, Gemini, Perplexity, Grok) in calculating Cobb angles from scoliosis radiographs.
  • To compare the performance of these LLMs against a radiologist's measurements.
  • To evaluate the usability and clinical applicability of AI in Cobb angle determination.

Main Methods:

  • A cross-sectional study involving 122 scoliosis patient radiographs.
  • Independent Cobb angle calculation by a fellowship-trained radiologist using Horos software as the reference standard.
  • Radiographs were analyzed by four LLMs to identify scoliosis type, generate overlays, and calculate Cobb angles, with subsequent statistical analysis.

Main Results:

  • Gemini and ChatGPT showed significant limitations, with Gemini failing to produce measurements and ChatGPT exhibiting large errors (MAE 58.6° ± 45.9°).
  • Perplexity and Grok provided estimates for all cases, but with mean differences of 18.8° (±13.3°) and 24.2° (±18.3°), respectively.
  • No AI model accurately identified S-shaped scoliosis or met the clinically acceptable difference (≤10%) for Cobb angle measurement.

Conclusions:

  • Commercially available LLMs currently exhibit insufficient accuracy for clinical Cobb angle measurement in scoliosis.
  • While Perplexity and Grok showed better performance among the tested models, none achieved clinically acceptable accuracy.
  • Development of specialized AI tools is necessary for reliable clinical integration in spinal deformity assessment.