Automated Scoliosis Diagnosis in Spinal Imaging: Laboratory Validation, Clinical Limitations, and Systematic Implementation Challenge Review

Technological advances in automated medical imaging diagnosis have created translation gaps between laboratory achievements and clinical implementation, with traditional manual Cobb angle measurement requiring considerable time with inevitable measurement errors. This review analyzes translation challenges in automated diagnosis systems using scoliosis assessment as a case study, examining 55 articles from 1948-2025 across three domains: Cobb angle measurement, classification, and segmentation. Despite research investment, fully automated approaches have not surpassed semi-automated performance in comparable validation studies. Within the 23 Cobb angle measurement studies, traditional methods outperform sophisticated deep learning systems with average error rates of 1.8° ± 0.4° MAD versus 4.2° ± 1.8° MAE, while validation degradation occurs with performance dropping from 95.28% to 85.9% when transitioning to real-world datasets. Nonstandard classification achieves high accuracy but lacks clinical utility, while standard systems struggle with automation, revealing a translation paradox where technical sophistication does not correlate with clinical adoptability. Main problems include testing method gaps, performance drops, different automation approaches, and cost issues. This review recommends standard testing methods and step-by-step clinical implementation to help these systems work in real clinics.