This study aimed to investigate the accuracy of large language models (LLMs), specifically ChatGPT and Claude, in surgical decision-making and radiological assessment for spine pathologies compared to experienced spine surgeons. The study employed a comparative analysis between the LLMs and a panel of attending spine surgeons. Five written clinical scenarios encompassing various spine pathologies were presented to the LLMs and surgeons, who provided recommended surgical treatment plans. Additionally, magnetic resonance imaging images depicting spine pathologies were analyzed by the LLMs and surgeons to assess their radiological interpretation abilities. Spino-pelvic parameters were estimated from a scoliosis radiograph by the LLMs. Qualitative content analysis revealed limitations in the LLMs' consideration of patient-specific factors and the breadth of treatment options. Both ChatGPT and Claude provided detailed descriptions of magnetic resonance imaging findings but differed from the surgeons in terms of specific levels and severity of pathologies. The LLMs acknowledged the limitations of accurately measuring spino-pelvic parameters without specialized tools. The accuracy of surgical decision-making for the LLMs (20%) was lower than that of the attending surgeons (100%). Statistical analysis showed no significant differences in accuracy between the groups. The study highlights the potential of LLMs in assisting with radiological interpretation and surgical decision-making in spine surgery. However, the current limitations, such as the lack of consideration for patient-specific factors and inaccuracies in treatment recommendations, emphasize the need for further refinement and validation of these artificial intelligence (AI) models. Continued collaboration between AI researchers and clinical experts is crucial to address these challenges and realize the full potential of AI in spine surgery.

Neural tube defects (NTDs) are congenital abnormalities in the central nervous system. The exact etiology of NTDs is still not determined, but several genetic and epigenetic factors have been studied. Folate supplementation during gestation is recommended to reduce the risk of NTDs. In this review we examine single nucleotide polymorphisms (SNPs) of the genes in the folate pathway associated with NTD. We reviewed the literature for all papers discussing both NTDs and SNPs in the folate pathway. Data were represented through five different genetic models. Quality assessment was performed using the Newcastle–Ottawa Scale (NOS) and Cohen’s Kappa inter-rater coefficient assessed author agreement. Fifty-nine papers were included. SNPs in MTHFR, MTRR, RFC genes were found to be highly associated with NTD risk. NOS showed that high quality papers were selected, and Kappa Q-test was 0.86. Our combined results support the notion that SNPs significantly influence NTDs across the population, particularly in Asian ethnicity. Additional high-quality research from diverse ethnicities is needed and meta-regression analysis based on a range of criteria may provide a more complete understanding of the role of folate metabolism in NTDs.

Study Design.: Meta-analysis. Objective.: To determine the single nucleotide polymorphisms (SNPs) that are related to adult idiopathic scoliosis. Summary and Background Data.: Adolescent idiopathic scoliosis (AIS) is considered one of the most prevalent spinal diseases. Even though the cause of AIS is yet to be determined, family history and sex have shown conclusive associations. Multiple studies have indicated that AIS is more prevalent in families where at least one other first-degree relative is similarly affected, indicating a possible genetic etiology to AIS. Materials and Methods.: Articles were collected from 3 different search engines and then processed in 2 stages for final article selection for quantitative analysis. Five different genetic models were represented to show the association between the different SNPs and AIS. The Hardy-Weinberg equilibrium was examined using Fisher exact test, with significance set at P C and MATN-1 SNPs were associated with an increased risk of AIS in one or all of the 5 genetic models. IGF-1, estrogen receptor alfa, and MTNR1B, SNPs were not associated with AIS in all 5 genetic models. Newcastle Ottawa Scale showed good quality for the selected articles. Cohen k = 0.741 and Kappa interrater agreement of 84% showed that the writers were in strong agreement. Conclusions.: There seem to be associations between AIS and genetic SNP. Further larger studies should be conducted to validate the results.

Aim: Quantitative measurement of spinopelvic parameters from radiographs is important for assessing spinal disorders but is limited by the subjectivity and inefficiency of manual techniques. Deep learning may enable automated measurement with accuracy rivaling human readers. Methods: PubMed, Embase, Scopus, and Cochrane databases were searched for relevant studies. Eligible studies were published in English, used deep learning for automated spinopelvic measurement from radiographs, and reported performance against human raters. Mean absolute errors and correlation coefficients were pooled in a meta-analysis. Results: Fifteen studies analyzing over 10,000 radiographs met the inclusion criteria, employing convolutional neural networks (CNNs) and other deep learning architectures. Pooled mean absolute errors were 4.3° [95% confidence interval (CI) 3.2-5.4] for Cobb angle, 3.9° (95%CI 2.7-5.1) for thoracic kyphosis, 3.6° (95%CI 2.8-4.4) for lumbar lordosis, 1.9° (95%CI 1.3-2.5) for pelvic tilt (PT), 4.1° (95%CI 2.7-5.5) for pelvic incidence (PI), and 1.3 cm (95%CI 0.9-1.7) for sagittal vertical axis (SVA). Intraclass correlation coefficients exceeded 0.81, indicating strong agreement between automated and manual measurements. Conclusion: Deep learning demonstrates promising accuracy for automated spinopelvic measurement, potentially rivaling experienced human readers. However, further optimization and rigorous multicenter validation are required before clinical implementation. These technologies may eventually improve the efficiency and reliability of quantitative spine image analysis.