# AI applications in lumbar and lumbosacral pedicle screw placement: a systematic review of limited evidence and future directions

## TL;DR

This paper reviews how AI can help with placing screws in the spine during surgery, highlighting benefits like faster operations and reduced errors.

## Contribution

The paper systematically categorizes and analyzes current AI applications for pedicle screw placement in spine surgery.

## Key findings

- AI reduces operation time and improves accuracy in identifying anatomical landmarks.
- AI applications help reduce radiation exposure and image errors during surgery.
- Limited training data and lack of diversity hinder AI model generalization and robustness.

## Abstract

Artificial intelligence (AI) is a general term that refers to the use of a computer to simulate intelligent behavior with minimal human intervention. Currently, AI can be applied to various spine surgery approaches. This review aims to provide a clearer picture of AI’s applicability for the perioperative period and enhance outcomes for pedicle screw fixation (PS). The PRISMA guideline was applied, which identified 14 studies regarding AI applications in PS. We categorized the AI application to PS into segmentation, object detection, image registration, and other categories, such as improved quality and converted images. Then, an analysis and discussion of the current trends and applications of various AI models in PS methods was performed. The effects of AI performance included a reduction in the time required for operations and planning, automatic identification of screws and anatomical landmarks, reduced image errors, and reduced radiation exposure. However, the lack of training data and less data diversity remain the limitations of model development, as both factors impact model generalization and robustness. This data extraction might reveal research gaps, providing researchers with ideas for future studies regarding AI and PS integration for better medical care outcomes.

The online version contains supplementary material available at 10.1007/s10143-026-04192-2.