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AI assisted photographic posture evaluation: a comparative study of ChatGPT and expert physiotherapists
Ugur Sozlu1, Selim Mahmut Gunay2, Gul Ozkazanc3
1Department of Physical Therapy and Rehabilitation, Faculty of Health Sciences, Tokat Gaziosmanpasa University, Taşlıçiftlik Yerleşkesi, Tokat, 60250, Turkey. ugur.sozlu@gop.edu.tr.
Background:
Traditional visual methods used in posture analysis are limited by subjectivity and variability, whereas computer vision systems provide precise geometric measurements but require technical expertise and specialized resources. Large language models like ChatGPT offer an accessible, low-cost alternative with human-like interpretive abilities and potential educational value. This study aimed to examine the agreement between ChatGPT and expert physiotherapists in standardized photographic posture assessments.
Methods:
This cross-sectional comparative study included 39 young adults with at least one visible postural deviation. The sampling frame consisted of individuals preliminarily evaluated during a posture assessment assignment by physiotherapy students. Standardized anterior, posterior, and lateral photographs were taken under controlled conditions. Two physiotherapists independently assessed posture using a seven-region Posture Analysis Form, and the same anonymized photographs were evaluated by ChatGPT-5.1 (multimodal version, web interface, 15-16 November 2025) using a standardized prompt.
Results:
Inter-rater agreement between physiotherapists was excellent (κ = 0.84-0.94). Agreement between physiotherapists and ChatGPT ranged from moderate to excellent. Trunk (κ = 0.85) and knee alignment (κ = 0.83) showed almost perfect agreement, while head-neck position showed substantial agreement (κ = 0.67). In contrast, scapular (κ = 0.51), shoulder girdle (κ = 0.47), pelvic (κ = 0.47), and foot alignment (κ = 0.43) exhibited moderate agreement. Percentage agreement ranged from 64.10% to 92.31%.
Conclusion:
This study suggested that ChatGPT shows high agreement with physiotherapists in regions with clearly observable postural deviations, while its reliability decreases in areas requiring finer angular discrimination. The findings further indicate that the model may serve as a supportive tool, particularly for assessing trunk, knee, and head-neck alignment.
