The accurate extraction of surgical data from electronic health records (EHRs), particularly operative notes through manual chart review (MCR), is complex, crucial, and time-intensive, limited by human error due to fatigue and the level of training. This study aimed to develop and validate a novel Natural Language Processing (NLP) algorithm integrated with a Large Language Model (LLM; GPT4-Turbo) to automate the extraction of spinal surgery data from EHRs. The algorithm employed a two-stage approach. Initially, a rule-based NLP framework reviewed and classified candidate segments from the text, preserving their reference segments. These segments were then verified in the second stage through the LLM. The primary outcomes of this study were the accurate extraction of surgical data, including the type of surgery, levels operated, number of disks removed, and presence of intraoperative incidental durotomies. Secondary objectives explored time efficiency, tokenization lengths, and costs. The performance of the algorithm was assessed across two validation databases, analyzing metrics such as accuracy, sensitivity, discrimination, F1-score, and precision, with 95% confidence intervals calculated using percentile-based bootstrapping. The NLP + LLM algorithm markedly outperformed all performance metrics, demonstrating significant improvements in time and cost efficiency. These results suggest the potential for widespread adoption of this technology.

Interest in the use of artificial intelligence (AI) and machine learning (ML) in medicine has grown exponentially over the last few years. With its ability to enhance speed, precision, and efficiency, AI has immense potential, especially in the field of surgery. This article aims to provide a comprehensive literature review of artificial intelligence as it applies to surgery and discuss practical examples, current applications, and challenges to the adoption of this technology. Furthermore, we elaborate on the utility of natural language processing and computer vision in improving surgical outcomes, research, and patient care.

Study Design: : Retrospective Chart Review. Objectives: Incidental durotomies (IDs) are common spine surgery complications. In this study, we present a review on the most commonly utilized management strategies, report our institutional experience with case examples, and describe a stepwise management algorithm. Methods: A retrospective review was performed of the electronic medical records of all patients who underwent a thoracolumbar or lumbar spine surgery between March 2017 and September 2019. Additionally, a literature review of the current management approaches to treat IDs and persistent postoperative CSF leaks following lumbar spine surgeries was performed. Results: We looked at 1133 patients that underwent posterior thoracolumbar spine surgery. There was intraoperative evidence of ID in 116 cases. Based on our cohort and the current literature, we developed a progressive treatment algorithm for IDs that begins with a primary repair, which can be bolstered by dural sealants or a muscle patch. If this fails, the primary repair can be followed by a paraspinal muscle flap, as well as a lumbar drain. If the patient cannot be weaned from temporary CSF diversion, the final step in controlling postoperative leak is longterm CSF diversion via a lumboperitoneal shunt. In our experience, these shunts can be weaned once the patient has no further clinical or radiographic signs of CSF leak. Conclusions: There is no standardized management approach of IDs and CSF leaks in the literature. This article intends to provide a progressive treatment algorithm and contribute to the development process of a treatment consensus.

This cross-sectional study evaluates the clinical accuracy, relevance, clarity, and emotional sensitivity of responses to inquiries from patients undergoing surgery provided by large language models (LLMs), highlighting their potential as adjunct tools in patient communication and education. Our findings demonstrated high performance of LLMs across accuracy, relevance, clarity, and emotional sensitivity, with Anthropic’s Claude 2 outperforming OpenAI’s ChatGPT and Google’s Bard, suggesting LLMs’ potential to serve as complementary tools for enhanced information delivery and patient-surgeon interaction.

Objective: The development of adjacent segment disease or the progression of spondylosis following the surgical treatment of spinal stenosis and spondylolisthesis is well documented and can lead to subsequent functional decline after a successful index surgery. The early detection of negative inflection points during patients’ functional recovery can improve timely intervention. In this study, we developed machine learning (ML) models to predict the occurrence of post-operative decline in patient mobility. Methods: Patients receiving spine surgery for degenerative spinal stenosis or spondylolisthesis were retroactively consented and enrolled. Activity data (steps-per-day) previously recorded across a 4-year peri-operative were collected alongside relevant clinical and demographic variables. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) ML models were constructed and trained on 80% of the dataset and validated using the remaining 20%. The study’s primary endpoint was the models’ ability to predict post-operative decline in patient mobility. Results: A total of 75 patients were included. Following training, RF and XGBoost models achieved accuracy values of 86.7% (sensitivity 80%, specificity 90%) and 80% (sensitivity 60%, specificity 90%), respectively, in predicting post-operative functional decline. The LR model was the least effective with an accuracy of 73.3% (sensitivity 50%, specificity 88.8%). Receiver operating characteristic curves showed an area under the curve of 0.80 for RF, 0.70 for XGBoost, and 0.69 for LR. Conclusions: ML models trained on activity data collected from smartphones successfully forecast functional decline in post-operative activity following spine surgery. These results lay the groundwork for the future integration of ML into the surgeon’s toolbox for prognostication and surgical planning.