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April 15, 2026Spine Deformity7 citationsOpen Access

Risk prediction in spine surgery: a scoping review of traditional models, artificial intelligence, and the challenge of clinical translation

SSSamer SalmanRPRohan PhadkeRKRahul Kumar

Key Points

  • This review aims to evaluate the effectiveness of traditional and AI risk prediction models in spine surgery.
  • Performed a scoping review of existing literature on risk prediction models for spine surgery.
  • Assessed traditional models and AI/machine learning approaches for their performance and usability.
  • Analyzed challenges related to validation and integration in clinical settings.
  • Traditional risk models are still seen as reliable and interpretable for spine surgery outcomes.
  • AI/ML approaches improve data handling but face challenges in clinical validation and trust.
  • Future success requires rigorous testing and seamless integration into clinical workflows.

Abstract

Traditional risk models remain interpretable, trusted, and competitively performant for many spine surgery outcomes. While AI/ML approaches expand data integration and interaction modeling, their clinical impact is constrained by validation, trust, and implementation barriers. Future progress will depend less on incremental performance gains and more on rigorous external validation, prospective outcome studies, and integration into clinical workflows.

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Cite This Study

Salman et al. (2026) studied this question.

synapsesocial.com/papers/69df2bcae4eeef8a2a6b0b0fhttps://doi.org/10.1007/s43390-026-01365-3
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