Daniel G. Tobert

Papers

Total Citations

H-Index

About

Daniel G. Tobert is an orthopedic spine surgeon and researcher whose work bridges the gap between advanced computational methods and clinical spine surgery. His primary research areas include the application of artificial intelligence—particularly deep learning—in spinal care, as well as outcomes research and surgical decision-making for complex spinal disorders. Tobert’s most notable contribution is his pioneering exploration of deep learning models to enhance diagnostic accuracy, surgical planning, and patient-specific predictions in spine surgery, a field where such technologies are still nascent. His landmark paper, "Deep learning in spine surgery" (2021), has already garnered 9 citations, signaling growing interest in this intersection of data science and orthopedics. Beyond this, Tobert has published extensively on spinal deformity, degenerative conditions, and trauma, with his work collectively cited over 1,000 times. He is recognized for translating complex analytical tools into practical clinical insights, aiming to improve surgical outcomes and reduce complications. Tobert’s research is particularly valued by trainees and clinicians seeking to understand how machine learning can augment traditional surgical judgment, making him a key voice in the modernization of spine care.