This study evaluates the effectiveness of artificial intelligence (AI) models in generating accurate, high-quality medical illustrations for vascular neurosurgery. It aims to develop a systematic framework for producing and assessing AI-generated medical images. Four AI models-DALL-E, Copilot, Gemini, and Midjourney-were tested to generate illustrations of neurovascular structures and procedures (e.g., aneurysms, endovascular techniques). The study had three stages: (1) Proof of concept, in which each model generated images for nine neurovascular topics, evaluated using a standardized rubric; (2) A focused comparison using simple vs. advanced prompt strategies with Gemini; and (3) Validation of the best strategy with neurosurgery trainees and attendings. Images were scored from 1 (worst) to 5 (best) across eight domains: Accuracy, Location, Size/Scale, Color, Complexity, Educational Value, Relevance, and Aesthetic Quality. Gemini consistently outperformed other models in Stage 1, particularly in accuracy, color, and educational value. In Stage 2, advanced prompting significantly improved image quality across nearly all topics (e.g., fusiform aneurysm score rose from 22.4 to 35.0, p = 7E-08). In Stage 3, 85 % of respondents indicated they would use the saccular aneurysm image in a manuscript without modification. However, complex anatomy like anterior cerebral arteries scored lower in accuracy (2.18) and educational value (2.20). AI-generated illustrations, especially from Gemini, show strong potential in neurosurgical education and communication. While advanced prompting improves output quality, challenges remain in consistently rendering complex anatomy. This study outlines a reproducible framework for clinical integration of AI-generated medical images.

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.