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From Pixels to Precision: Generative Artificial Intelligence as a Paradigm Shift in Spine Imaging-Technical
Daniyal Ashraf1, Vivek Sanker2, Linda Liverani2
1School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.
Neurospine
|May 7, 2026
Summary
Generative AI (GenAI) shows promise in spine imaging by creating synthetic data for faster scans and radiation-free planning. However, challenges like validation gaps and bias hinder clinical use, requiring multi-institutional data and better validation.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Spine Diagnostics
Background:
- Spine imaging faces challenges like anatomical variability and inter-reader variability, limiting conventional AI.
- Current discriminative AI models struggle with data scarcity, heterogeneous protocols, and generalizability in spine imaging.
- Subtle distinctions in spine imaging are crucial for clinical decisions, making AI limitations impactful.
Purpose of the Study:
- To review generative artificial intelligence (GenAI) applications in spine imaging.
- To explore GenAI's potential in image reconstruction, synthetic data generation, segmentation, and surgical planning.
- To identify barriers and future directions for GenAI in clinical spine imaging.
Main Methods:
- Narrative review using SANRA methodology.
- Searched PubMed, Scopus, Embase, and Cochrane Library for eligible studies.
- Included observational designs through randomized controlled trials on GenAI in spine imaging.
Main Results:
- Generative adversarial networks (GANs) reduce MRI scan times by ~40%.
- Diffusion models enable radiation-free synthetic CT for preoperative planning.
- Vision-language models (VLMs) generate structured reports with low hallucination rates (<1.12%).
Conclusions:
- GenAI offers significant potential for spine imaging, including faster scans and improved planning.
- Clinical translation is hindered by validation gaps, hallucinations, bias, and technical/regulatory hurdles.
- Priorities for integration include multi-institutional data, federated learning, explainability, clinical validation, and workflow integration.