Dr. Jun S. Kim is a fellowship trained spine surgeon at The Mount Sinai Hospital and an Assistant Professor in the Department of Orthopedic Surgery. Dr. Kim specializes in cervical spine and scoliosis surgery, using a combination of minimally invasive, open, and microsurgical techniques to treat disorders of the cervical, thoracic, and lumbar spine. Dr. Kim attended Cornell University and graduated with Bachelor of Science in Chemical and Biomolecular Engineering. He received his MD from the Lewis Katz School of Medicine at Temple University, and completed orthopaedic surgery residency at Mount Sinai Medical Center. After his residency at Mount Sinai, Dr. Kim completed a spine fellowship in adult and pediatric scoliosis and deformity surgery at Columbia University Medical Center where he served as an assistant attending physician and a clinical instructor.

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Dr. Kim believes that modern spine care should be comprehensive and multidisciplinary. He believes that this comes from a team approach comprised of physiatrists, physical therapists, anesthesiologists, neurologists, radiologists, internists, and pediatricians. Secondly, he believes it is imperative that all non-surgical avenues be explored and trialed before surgery is considered. Lastly, if and when surgery becomes necessary, Dr. Kim believes the surgery should be personalized and tailored to the individual patient.

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Dr. Kim has trained with both orthopaedic spine surgeons and neurosurgical spine surgeons. During the course of these years, he has had experience successfully treating advanced scoliosis and deformities of the spine in adult and pediatric patients.

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Dr. Kim is also the head of the Orthopaedic Surgery Department’s Artificial Intelligence (AI) and Machine Learning (ML) laboratory and a member of the Mount Sinai AI Consortium. He has published numerous peer-reviewed articles utilizing deep learning for diagnostics, prognostication and risk stratification. He is currently one of the first orthopaedic surgeons to employ and publish on machine learning techniques to study, analyze, and improve the way surgeons practice medicine. He has won numerous grants in the space of AI, and He was nominated by the Scoliosis Research Society for the prestigious Russell A. Hibbs Clinical Research Award.

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