Mount Sinai researchers develop promising AI-driven surgical education model to improve quality of resident training
Study suggests significant cost savings and reductions in errors
The Mount Sinai Hospital / Mount Sinai School of Medicine
New York, NY (August 6, 2025) – Mount Sinai researchers have demonstrated the effectiveness of teaching surgical trainees a difficult procedure using artificial intelligence (AI) algorithms and an extended-reality headset without the presence of an instructor. All of the 17 trainees in the study achieved surgical success.
The novel study, published in Journal of Medical Extended Reality, drew highly favorable reviews from student participants who tested the deep learning model. The results carry significant implications for future training of residents and surgeons, as well as for the even broader field of autonomous learning within medicine.
“For the first time, we created an AI model linked to an extended-reality headset to prove that a critical step in a kidney cancer procedure could be done with 99.9 percent accuracy,” said Nelson Stone, MD, Clinical Professor of Urology, Radiation Oncology, and Oncological Sciences at the Icahn School of Medicine at Mount Sinai, and corresponding author of the study. “We believe our study offers early proof that AI programs that substitute for proctors, who teach resident physicians, can reduce training costs and ultimately improve the quality, efficiency, and standardization of that instruction.”
Surgical training of residents has traditionally required the presence of a teaching proctor alongside the student physician in the operating room, which can result in inconsistent skills acquisition. Dr. Stone and his team, which included researchers from the Department of Neurosurgery at the University of Rochester Medical Center in upstate New York, explored an alternative training system using AI programs they developed, including ESIST (educational system for instructionless surgical training). This model coupled deep learning methodology with a custom-designed extended-reality headset worn by the 17 participants to stream surgical instructions and video content before their eyes, while allowing their hands to remain free to practice the intricate procedure.
The operation simulated a partial nephrectomy procedure designed to remove a cancerous portion of a kidney, including placing a clamp on the renal artery. For this replication, researchers created a “phantom” kidney from 3D printed casts of an anonymized patient’s computerized tomography (CT) scans. The casts were filled with water-based polymers and assembled to create a partial nephrectomy model with kidney tumors. While students practiced, the system’s sophisticated first-person camera continuously monitored their training, providing real-time feedback and projecting corrective prompts as part of its skills assessment capability.
“Above all, our study proved that a complex procedure like a partial nephrectomy could be effectively taught to surgical trainees using a simulated model, without the presence of an instructor,” noted Dr. Stone. “This finding addresses an urgent need resulting from the shortage of trainers and supervisors to educate physicians on new medical devices and techniques, and from the severe time constraints on attending physicians to train residents pursing surgical careers.”
Another major advantage of advanced teaching technology, added Dr. Stone, is that it allows future surgeons to become proficient in procedures outside the operating room, thus helping to reduce the risk of surgical errors. “From the patient’s point of view, we hope this study will provide reassurance that the technology can be leveraged to greatly improve surgical proficiency, while reducing surgical errors,” said Dr. Stone.
The next step for Mount Sinai researchers is to use the AI algorithm technology they developed to build more complex synthetic cadaver models to train students in entire procedures, rather than just one component, as reported in the study. The team was encouraged by a survey it conducted after the training, which found that 100 percent of the participants believed the program had great educational value.
“Our investigation suggests that AI systems could indeed play an important complementary role in shaping the future of surgical education in this country,” asserts Dr. Stone. “The public should be reassured that the pathway to autonomous learning we investigated in this small study could eventually lead to significant cost savings and improved patient outcomes and, importantly, to the cultivation of a highly skilled new generation of surgeons.”
The study’s authors, as listed in the journal, are Jonathan J. Stone, Nelson N. Stone, Steven H. Griffith, Kyle Zeller, and Michael P. Wilson.
All authors, except Kyle Zeller, hold equity in Viomerse.
The research was funded by the National Institute of Biomedical Imaging and Bioengineering (grant 1R41EB026358-01A1) and the National Science Foundation (grant 1913911).
About the Mount Sinai Health System
Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with 48,000 employees working across seven hospitals, more than 400 outpatient practices, more than 600 research and clinical labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time—discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it.
Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients’ medical and emotional needs at the center of all treatment. The Health System includes approximately 9,000 primary and specialty care physicians and 11 free-standing joint-venture centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida. Hospitals within the System are consistently ranked by Newsweek’s® “The World’s Best Smart Hospitals, Best in State Hospitals, World Best Hospitals and Best Specialty Hospitals” and by U.S. News & World Report's® “Best Hospitals” and “Best Children’s Hospitals.” The Mount Sinai Hospital is on the U.S. News & World Report® “Best Hospitals” Honor Roll for 2025-2026.
For more information, visit https://www.mountsinai.org or find Mount Sinai on Facebook, Instagram, LinkedIn, X, and YouTube.
Journal
Journal of Medical Extended Reality
Method of Research
Computational simulation/modeling
Subject of Research
Not applicable
Article Title
Autonomous Educational System for Surgical Training Utilizing Deep Learning Combined with Extended Reality
Human instruction with artificial intelligence guidance provided best results in neurosurgical training
Study has implications beyond medical education, suggesting other fields could benefit from AI-enhanced training
image:
A VR simulator of neurosurgery
view moreCredit: The Neuro (Montreal Neurological Institute-Hospital)
Artificial intelligence (AI) is becoming a powerful new tool in training and education, including in the field of neurosurgery. Yet a new study suggests that AI tutoring provides better results when paired with human instruction.
Researchers at the Neurosurgical Simulation and Artificial Intelligence Learning Centre at The Neuro (Montreal Neurological Institute-Hospital) of McGill University are studying how AI and virtual reality (VR) can improve the training and performance of brain surgeons. They simulate brain surgeries using VR, monitor students’ performance using AI and provide continuous verbal feedback on how students can improve performance and prevent errors. Previous research has shown that an intelligent tutoring system powered by AI developed at the Centre outperformed expert human teachers, but these instructors were not provided with trainee AI performance data.
In their most recent study, the researchers recruited 87 medical students from four Quebec medical schools and divided them into three groups: one trained with AI-only verbal feedback, one with expert instructor feedback, and one with expert feedback informed by real-time AI performance data. The team recorded the students’ performance, including how well and how quickly their surgical skills improved while undergoing the different types of training.
They found that students receiving AI-augmented, personalized feedback from a human instructor outperformed both other groups in surgical performance and skill transfer. This group also demonstrated significantly better risk management for bleeding and tissue injury—two critical measures of surgical expertise. The study suggests that while intelligent tutoring systems can provide standardized, data-driven assessments, the integration of human expertise enhances engagement and ensures that feedback is contextualized and adaptive.
“Our findings underscore the importance of human input in AI-driven surgical education,” said lead study author Bianca Giglio. “When expert instructors used AI performance data to deliver tailored, real-time feedback, trainees learned faster and transferred their skills more effectively.”
While this study was specific to neurosurgical training, its findings could carry over to other professions where students must acquire highly technical and complex skills in high-pressure environments.
“AI is not replacing educators—it’s empowering them,” added senior author Dr. Rolando Del Maestro, a neurosurgeon and current Director of the Centre, “by merging AI’s analytical power with the critical guidance of experienced instructors, we are moving closer to creating the “Intelligent Operating Room” of the future capable of assessing and training learners while minimizing errors during human surgical procedures.”
The study was published in the journal JAMA Surgery on Aug. 6, 2025. It was funded by the Brain Tumour Foundation of Canada, the Royal College of Physicians and Surgeons of Canada, a Mitacs Accelerate Grant, the Franco Di Giovanni Foundation, Canadian Graduate Scholarships, Le Fonds de recherche du Québec – Santé, and a McGill University Max Binz Fellowship.
About The Neuro
The Neuro – The Montreal Neurological Institute-Hospital – is a bilingual, world-leading destination for brain research and advanced patient care. Since its founding in 1934 by renowned neurosurgeon Dr. Wilder Penfield, it has grown to be the largest specialized neuroscience research and clinical center in Canada, and one of the largest in the world. The seamless integration of research, patient care, and training of the world’s top minds make The Neuro uniquely positioned to have a significant impact on the understanding and treatment of nervous system disorders. It was the first academic institute in the world to fully adopt Open Science, to help accelerate the generation of knowledge and discovery of novel effective treatments for brain disorders. The Neuro is a McGill University research and teaching institute and part of the Neuroscience Mission of the McGill University Health Centre. For more information, please visit www.theneuro.ca
Journal
JAMA Surgery
Method of Research
Experimental study
Subject of Research
People
Article Title
Artificial intelligence augmented human instruction and surgical simulation performance
Article Publication Date
6-Aug-2025
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