Large Language Models (LLMs have the potential to revolutionize clinical medicine by enhancing healthcare access, diagnosis, surgical planning, and education. However, their utilization requires careful, prompt engineering to mitigate challenges like hallucinations and biases. Proper utilization of LLMs involves understanding foundational concepts such as tokenization, embeddings, and attention mechanisms, alongside strategic prompting techniques to ensure accurate outputs. For innovative healthcare solutions, it is essential to maintain ongoing collaboration between AI technology and medical professionals. Ethical considerations, including data security and bias mitigation, are critical to their application. By leveraging LLMs as supplementary resources in research and education, we can enhance learning and support knowledge-based inquiries, ultimately advancing the quality and accessibility of medical care. Continued research and development are necessary to fully realize the potential of LLMs in transforming healthcare.

➤ Sagittal alignment of the spine has gained attention in the field of spinal deformity surgery for decades. However, emerging data support the importance of restoring segmental lumbar lordosis and lumbar spinal shape according to the pelvic morphology when surgically addressing degenerative lumbar pathologies such as degenerative disc disease and spondylolisthesis. ➤ The distribution of caudal lordosis (L4-S1) and cranial lordosis (L1-L4) as a percentage of global lordosis varies by pelvic incidence (PI), with cephalad lordosis increasing its contribution to total lordosis as PI increases. ➤ Spinal fusion may lead to iatrogenic deformity if performed without attention to lordosis magnitude and location in the lumbar spine. ➤ A solid foundation of knowledge with regard to optimal spinal sagittal alignment is beneficial when performing lumbar spinal surgery, and thoughtful planning and execution of lumbar fusions with a focus on alignment may improve patient outcomes.

➤ Machine learning is a subset of artificial intelligence in which computer algorithms are trained to make classifications and predictions based on patterns in data. The utilization of these techniques is rapidly expanding in the field of orthopaedic research. ➤ There are several domains in which machine learning has application to orthopaedics, including radiographic diagnosis, gait analysis, implant identification, and patient outcome prediction. ➤ Several limitations prevent the widespread use of machine learning in the daily clinical environment. However, future work can overcome these issues and enable machine learning tools to be a useful adjunct for orthopaedic surgeons in their clinical decision-making.

Background: In today’s digital age, patients increasingly rely on online search engines for medical information. The integration of large language models such as GPT-4 into search engines such as Bing raises concerns over the potential transmission of misinformation when patients search for information online regarding spine surgery. Methods: SearchResponse.io, a database that archives People Also Ask (PAA) data from Google, was utilized to determine the most popular patient questions regarding 4 specific spine surgery topics: anterior cervical discectomy and fusion, lumbar fusion, laminectomy, and spinal deformity. Bing’s responses to these questions, along with the cited sources, were recorded for analysis. Two fellowship-trained spine surgeons assessed the accuracy of the answers on a 6-point scale and the completeness of the answers on a 3-point scale. Inaccurate answers were re-queried 2 weeks later. Cited sources were categorized and evaluated against Journal of the American Medical Association (JAMA) benchmark criteria. Interrater reliability was measured with use of the kappa statistic. A linear regression analysis was utilized to explore the relationship between answer accuracy and the type of source, number of sources, and mean JAMA benchmark score. Results: Bing’s responses to 71 PAA questions were analyzed. The average completeness score was 2.03 (standard deviation [SD], 0.36), and the average accuracy score was 4.49 (SD, 1.10). Among the question topics, spinal deformity had the lowest mean completeness score. Re-querying the questions that initially had answers with low accuracy scores resulted in responses with improved accuracy. Among the cited sources, commercial sources were the most prevalent. The JAMA benchmark score across all sources averaged 2.63. Government sources had the highest mean benchmark score (3.30), whereas social media had the lowest (1.75). Conclusions: Bing’s answers were generally accurate and adequately complete, with incorrect responses rectified upon re-querying. The plurality of information was sourced from commercial websites. The type of source, number of sources, and mean JAMA benchmark score were not significantly correlated with answer accuracy. These findings underscore the importance of ongoing evaluation and improvement of large language models to ensure reliable and informative results for patients seeking information regarding spine surgery online amid the integration of these models in the search experience.