Douglas Kondziolka

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BACKGROUND AND OBJECTIVES: Multiple-choice questions are the primary assessment format for neurosurgical board certification. Creating high-quality examination questions requires significant expert time and resources. The goal of this study was to develop an automated system to generate board-style neurosurgical multiple-choice questions using state-of-the-art vision-language models and compare their quality with authentic self-assessment questions. METHODS: We developed an automated pipeline using OpenAI generative pre-trained transformer (GPT)-4o and Anthropic Claude Sonnet-3.5 to generate neurosurgical board-style questions from Neurosurgery Publications articles. We generated 89 587 synthetic questions: 45 689 with GPT-4o and 43 898 with Claude. Each question was associated with a single image extracted from the articles' figures. We evaluated the quality of synthetic questions through 5 surveys comparing 20 synthetic questions (10 from each model) with 10 authentic questions from the Self-Assessment for Neurological Surgeons (SANS) question bank. Each survey was completed by a neurosurgery resident and an attending who guessed the source [human vs artificial intelligence (AI)-generated] and rated suitability for board examination use. We also evaluated the question-answering performance of the generalist GPT-4o and the specialized CNS-Obsidian. RESULTS: SANS questions were more often perceived as human-made than GPT-generated (residents, P = .0002; attendings, P = .1091) and Claude-generated (residents, P = .0002; attendings, P = .0272) questions. Notably, 54% of AI-generated questions misled at least one evaluator, and 23% misled both. In quality assessments, SANS questions outperformed GPT-generated (residents, P < 10 ⁻⁵ ; attendings, P = .0001) and Claude-generated (residents and attendings, P < 10 ⁻⁵ ) questions. Particularly, 25% of AI-generated questions were rated as suitable for board examinations vs 72% of human-generated questions when measured by evaluator consensus ( P < 10 ⁻⁷ ). CONCLUSION: Although quality gaps exist between AI-generated and human-created neurosurgical board examination questions, our approach demonstrates the potential of vision-language models to augment assessment development in specialized medical fields, reducing the burden on examination boards and credentialing organizations.

BACKGROUND AND OBJECTIVES: Neurosurgery Publications encourages the creation of graphical abstracts to accompany published articles. The goal of this study was to develop a pipeline for the automatic conversion of Neurosurgery Publications articles into graphical abstracts using Cascade Styling Sheets (CSS) templates and iterative prompting of a frontier vision language model and to conduct a human evaluation of this pipeline. METHODS: We developed an automated pipeline to convert extracted manuscript content into standardized graphical abstracts. The pipeline implements a custom CSS profile designed to match existing journal standards. Using Claude Sonnet-3.5, we generated structured hypertext markup language summaries organized into 6 sections: Objectives, Background, Methods, Results, Discussion, and Conclusion. The model selected up to 2 representative figures per manuscript based on caption analysis. We evaluated performance using 100 randomly selected articles published between 2020 and 2024 (95 from Neurosurgery, 4 from Operative Neurosurgery, 1 from Neurosurgery Practice). Three Editorial Review Board members independently assessed abstracts using 3 binary criteria: (1) proper formatting, (2) factual accuracy, and (3) visual appeal. RESULTS: Generated graphical abstracts achieved proper formatting in 85% of cases (95% CI: 76.7%-90.7%), factual accuracy in 99% (95% CI: 94.4%-99.9%), and visual appropriateness in 82% (95% CI: 73.3%-88.3%). Overall, 70% of abstracts (95% CI: 60.5%-78.1%) met all 3 criteria and were deemed “publication ready” without manual intervention. Error analysis revealed poor figure selection (40.0%) as the most common failure mode, followed by title replacement errors from PDF extraction (26.7%). CONCLUSION: Our artificial intelligence-CSS pipeline demonstrates the feasibility of automating graphical abstract generation for neurosurgical manuscripts, achieving publication-ready quality in 70% of cases with 99% factual accuracy. This technology offers a scalable augmentation tool that can reduce the design burden for authors, enhancing visual scientific communication in neurosurgical publishing while complementing human expertise.

BACKGROUND AND OBJECTIVES: Traditional medical board examinations present clinical information in static vignettes with multiple-choices (MC), fundamentally different from how physicians gather and integrate data in practice. Recent advances in large language models (LLMs) offer promising approaches to creating more realistic clinical interactive conversations. However, these approaches are limited in neurosurgery, where patient communication capacity varies significantly and diagnosis heavily relies on objective data such as imaging and neurological examinations. We aimed to develop and evaluate a multi–artificial intelligence (AI) agent conversation framework for neurosurgical case assessment that enables realistic clinical interactions through simulated patients and structured access to objective clinical data. METHODS: We developed a framework to convert 608 Self-Assessment in Neurological Surgery first-order diagnosis questions into conversation sessions using 3 specialized AI agents: patient AI for subjective information, system AI for objective data, and clinical AI for diagnostic reasoning. We evaluated generative pretrained transformer 4o's (GPT-4o's) diagnostic accuracy across traditional vignettes, patient-only conversations, and patient + system AI interactions, with human benchmark testing from 10 neurosurgery residents. RESULTS: GPT-4o showed significant performance drops from traditional vignettes to conversational formats in both MC (89.0%-60.9%, P < .0001) and free-response scenarios (78.4%-30.3%, P < .0001). Adding access to objective data through system AI improved performance (to 67.4%, P = .0015; and 61.8%, P < .0001, respectively). Questions requiring image interpretation showed similar patterns but lower accuracy. Residents outperformed GPT-4o in free-response conversations (70.0% vs 28.3%, P = .0030) using fewer interactions and reported high educational value of the interactive format. CONCLUSION: This multi-AI agent framework provides both a more challenging evaluation method for LLMs and an engaging educational tool for neurosurgical training. The significant performance drops in conversational formats suggest that traditional MC testing may overestimate LLMs' clinical reasoning capabilities, while the framework's interactive nature offers promising applications for enhancing medical education.

BACKGROUND AND OBJECTIVES: Neurosurgical clinical research depends on medical data collection and evaluation that is often laborious, time consuming, and inefficient. The goal of this work was to implement and evaluate a novel departmental data infrastructure (Neuro Data Hub) designed to provide specialized data services for neurosurgical research. Data acquisition would become available purely by request. METHODS: We established the New York University Langone Health Neuro Data Hub through collaboration between Department Leadership and Medical Center Information Technology, integrating it with Institutional Review Board workflows and an existing Epic electronic health record Datalake infrastructure. The system implementation included monthly departmental meetings and an asynchronous Research Electronic Data Capture–based request system. Data requests submitted between August 2023 and November 2024 were analyzed and categorized as basic, complex, or Natural Language Processing (NLP)–augmented, with optional visualization and database creation services. Request volumes, types, and execution times were assessed. RESULTS: The Hub processed 39 research data requests (2.6/month), comprising 3 basic, 22 complex, and 14 NLP-augmented requests. Two complex requests included visualization services, and one NLP request included database creation. Average request execution time was 36.5 days, with NLP-augmented requests showing increasing adoption over time. CONCLUSION: The Neuro Data Hub represents a paradigm shift from centralized to department-level data services, providing specialized support for neurosurgical research and democratizing access to institutional data. While effective, implementation may be limited by institutional information technology infrastructure requirements. This model could serve as a template for any form of medical-clinical research program seeking to improve data accessibility and research capabilities.

Purpose Immune checkpoint inhibition (ICI) has revolutionized the treatment of melanoma care. Stereotactic radiosurgery combined with ICI has shown promise to improve clinical outcomes in prior studies in patients who have metastatic melanoma with brain metastases. However, others have suggested that concurrent ICI with stereotactic radiosurgery can increase the risk of complications. Methods We present a retrospective, single-institution analysis of 98 patients with a median follow up of 17.1 months managed with immune checkpoint inhibition and stereotactic radiosurgery concurrently and non-concurrently. A total of 55 patients were included in the concurrent group and 43 patients in the non-concurrent treatment group. Cox proportional hazards models were used to assess the relation between concurrent or non-concurrent treatment and overall survival or local progression-free survival. The Wald test was used to assess significance. Significant differences between patients in both groups experiencing adverse events including adverse radiation effects, perilesional edema, and neurological deficits were tested for using the Chi-square or Fisher’s exact test. Results Patients receiving concurrent versus non-concurrent ICI showed a significant increase in overall survival (median 37.1 months, 95% CI: 18.9 months - NA versus median 11.4 months, 95% CI: 6.4–33.2 months, p = 0.0056) but not local progression-free survival. There were no significant differences between groups with regards to adverse radiation effects (2% versus 3%), perilesional edema (20% versus 9%), neurological deficits (3% versus 20%). Conclusion These results suggest that the timing of ICI does not increase risk of neurological complications when delivered within 4 weeks of SRS.

BACKGROUND AND OBJECTIVES: Vestibular schwannomas demonstrate different responses after stereotactic radiosurgery (SRS), commonly including a transient loss of internal enhancement on postcontrast T1-weighted MRI thought to be due to an early reduction in tumor vascularity. We used dynamic contrast-enhanced based golden-angle radial sparse parallel (GRASP) MRI to characterize the vascular permeability changes underlying this phenomenon, with correlations to long-term tumor regression. METHODS: Consecutive patients with vestibular schwannoma who underwent SRS between 2017 and 2019, had a transient loss of enhancement after SRS, and had long-term longitudinal GRASP studies (6, 18, and 30 months) were included in this retrospective cohort analysis (n = 19). Using GRAVIS (https://gravis-imaging.org/gravis/), an analysis pipeline for GRASP studies, we extracted the key parameters normalized to the venous sinus from a region of interest within the tumor. RESULTS: The peak, area under the curve (AUC), and wash-in phase slope were significantly reduced at 6, 18, and 30 months after SRS (corrected P < .05), even while the internal enhancement returned in the tumors. Larger pre-SRS tumors were more likely to have a greater reduction in peak (P = .013) and AUC (P = .029) at 6 months. In a subset of patients (N = 13) with long-term follow-up, the median percentage reduction in tumor volume was 58% at a median of 62 months. These patients showed a strong correlation between peak, AUC, and wash-in phase slope changes at 6 months and tumor volume at the last follow-up. CONCLUSION: After SRS and loss of internal contrast uptake within vestibular schwannomas, a slow vascular permeability dynamic persisted, suggesting the presence of postradiation processes such as fibrosis. We show for the first time, using GRASP, a quantitative assessment of the vascular radiobiological effect.

BACKGROUND AND OBJECTIVES: Identifying and characterizing sources of targeting error in stereotactic procedures is essential to maximizing accuracy, potentially improving surgical outcomes. We aim to describe a generic framework which characterizes sources of stereotactic inaccuracy. METHODS: We assembled a list of stereotactic systems: ROSA, Neuromate, Mazor Renaissance, ExcelsiusGPS, Cirq, STarFix (FHC), Nexframe, ClearPoint, CRW, and Leksell. We searched the literature for qualitative and quantitative work identifying and quantifying potential sources of inaccuracy and describing each system's implementation using Standards for Reporting Qualitative Research guidelines. Our literature search spanned 1969 to 2024, and various studies were included, with formats ranging from phantom studies to systematic reviews. Keyword searches were conducted, and the details about each system were used to create a framework for identifying and describing the unique targeting error profile of each system. RESULTS: We describe and illustrate the details of various sources of stereotactic inaccuracies and generate a framework to unify these sources into a single framework. This framework entails 5 domains: imaging, registration, mechanical accuracy, target planning and adjustment, and trajectory planning and adjustment. This framework was applied to 10 stereotactic systems. CONCLUSION: This framework provides a rubric to analyze the sources of error for any stereotactic system. Illustrations allow the reader to understand sources of error conceptually so that they may apply them to their practice.