The future is now: How AI is reshaping spine care
Bibliographic record
Abstract
Introduction: Artificial intelligence (AI) is rapidly reshaping spinal care, transitioning from experimental algorithms to operational tools embedded in clinical workflows. This narrative review explores the evolving role of AI across the spine care continuum, including diagnostic imaging, surgical planning, intraoperative navigation, predictive analytics, and digital therapeutics. Methods: Using a mixed-methods approach, we synthesized insights from peer-reviewed literature, regulatory documents, press releases, and grey sources published between 2017 and 2025. Results: AI-enabled imaging platforms now assist in detecting spinal pathologies and extracting quantitative metrics such as disc heights and Cobb angles, improving diagnostic consistency and reducing inter-rater variability. In surgical planning, decision-support systems and robotics-integrated platforms offer personalized guidance and enhanced precision. Intraoperative tools using 2D-3D fusion and volumetric reconstruction are enabling hardware-light navigation, particularly in ambulatory settings. Predictive models for survival, reoperation risk, and patient-reported outcomes are emerging, though external validation remains limited.Digital therapeutics and wearable technologies are expanding the reach of spine care beyond the clinic, offering scalable solutions for rehabilitation and postoperative monitoring. Meanwhile, regulatory and legal frameworks are evolving to address transparency, data governance, and intellectual property concerns. Recent FDA guidance and landmark legal cases underscore the need for disciplined collaboration and responsible deployment. Conclusions: AI in spine care is no longer theoretical and is operational. Its integration promises enhanced precision, efficiency, and personalization, but also demands rigorous validation and ethical oversight. This review highlights the multifaceted impact of AI and calls for continued alignment between clinicians, developers, and regulators to ensure safe, equitable, and sustainable innovation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".