ReviewJournal of orthopaedic translation2026
Medical large language models and systems in the clinical application of spinal diseases: Current status, challenges, and future prospects.
Review in Journal of orthopaedic translation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Accuracy of Medical Imaging-Based Artificial Intelligence for Osteonecrosis of the Femoral Head: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Artificial Intelligence for Clinical Decision Support in Rural Spine Care: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Large Language Models in Spine Surgery : A Narrative Review of Performance Paradox and Clinical Integration Challenges.Journal of Korean Neurosurgical Society · 2026Article
- Large Language Models in Spine Surgery: A Scoping Review of Clinical Efficacy, Technical Integration, and Ethical Paradigms.Global spine journal · 2026Review
- Emerging era of robotics and artificial intelligence in orthopaedic trauma surgery.Journal of orthopaedic translation · 2026Article
- Integrating mechanistic research and emerging technologies to advance clinical translation in orthopaedics.Journal of orthopaedic translation · 2026Article
- Evaluation of large language model-generated information in diabetes health patient education: a scoping review.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large Language Models (LLMs), represented by the Generative Pretrained Transformer (GPT), are profoundly transforming the healthcare sector. Spine medicine, a discipline heavily reliant on complex imaging data, detailed clinical records, and evidence-based medical practice, serves as an ideal testing ground for exploring and applying these advanced artificial intelligence technologies. It holds the promise of optimizing clinical workflows, enhancing the quality of diagnosis and treatment decisions, and improving patient communication. We systematically searched PubMed and Embase from January 2023 to September 2025 for studies investigating LLMs in spinal diseases. Original research articles published in English with a Journal Impact Factor (JIF) ≥ 3.0 were included. Reviews, case reports, animal studies, and non-orthopedic topics were excluded. Data from eligible studies were extracted and narratively synthesized. This review aims to systematically and comprehensively examine the current state of clinical applications of medical large models and related intelligent systems in the field of spinal diseases. The focus is on analyzing their core technical pathways, specific clinical application scenarios, and their medical value, and performance evaluation results, thereby identifying current opportunities and key challenges. Furthermore, it anticipates future developments, from leveraging general-purpose models to constructing specialized models based on high-quality, large-scale, multimodal spine-specific datasets.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.