Dr. Jacob Young: “Neurosurgical Training, Artificial Intelligence, and Operative Video”

At SDSC’s Data Science Roundtable with Dr. Jacob Young – board-certified neurosurgeon and Assistant Professor in the Department of Neurological Surgery at University of California, San Francisco – explored how artificial intelligence (AI) and operative video analysis could transform neurosurgical training. He highlighted the growing challenges facing surgical education, including reduced resident autonomy, duty hour restrictions, increasing sub-specialization, and longer training pathways that now frequently extend into fellowship years.

Dr. Young explained that while the technical complexity of neurosurgery has advanced dramatically, training methods have remained largely unchanged for more than a century. The traditional apprenticeship-style learning model often provides trainees with subjective and inconsistent feedback, and limited opportunities for detailed technical review.

To address this gap, Dr. Young partnered with SDSC to develop AI-driven tools that analyze operative video recordings from microscopes, endoscopes, and surgical loupes. Using machine learning models, the team can track surgical instruments, assess efficiency metrics, evaluate focus and movement patterns, and generate objective feedback for trainees. Early work has shown promise in distinguishing expert from novice performance and identifying actionable opportunities for improvement.^1,2

The talk emphasized that AI-assisted coaching could enhance both resident education and early career surgeon development by delivering more personalized, objective, and timely feedback. Dr. Young concluded that integrating surgical video analytics into residency training has the potential to improve technical skill acquisition, reduce the teaching load on faculty members, and ultimately improve patient outcomes in neurosurgery.

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^{1. Surgical video machine learning platform to Quantify Surgical Performance Metrics: A new paradigm for neurosurgical training and feedback [Internet]. [cited 2026 May 12]. Available from: https://aans2025.eventscribe.net/fsPopup.asp?PosterID=724105&mode=posterInfo} 

^{2. Surgical video machine learning platform to quantify field of focus during posterior fossa craniotomies [Internet]. [cited 2026 May 12]. Available from: https://aans2025.eventscribe.net/fsPopup.asp?PosterID=724106&mode=posterInfo} 

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