Daniel S. Oh

Daniel S. Oh, MD is a Clinical Associate Professor of Surgery (Practitioner) at the Keck School of Medicine of the University of Southern California, specializing in general thoracic surgery with a focus on minimally invasive and robotic techniques. He serves as the medical director at the USC-St. Jude Center for Thoracic and Esophageal Diseases in Fullerton, where he sees patients exclusively. Dr. Oh has extensive experience in robotic-assisted thoracic procedures, with approximately 90 percent of his major operations performed using robotic assistance since 2011.

• Medical Degree: Not specified in text
• Surgical Training: USC and Harvard Medical School (Brigham and Women's Hospital)

Dr. Oh's research interests center on advancing robotic surgical techniques, particularly in thoracic and esophageal procedures. He actively collaborates with Intuitive Surgical to improve robotic technology, surgical training methods, and develop new procedures that enhance patient outcomes. His work emphasizes a disease-specific rather than technology-specific approach, making him unique among thoracic surgeons as he also performs advanced endoscopic and bronchoscopic procedures.

Analysis of Dr. Oh's recent publications reveals a strong focus on objective performance metrics in robotic surgery, surgical education methodologies, and optimizing robotic-assisted thoracic procedures. His research spans both clinical outcomes and technological innovations, with particular attention to quantifying surgical skill, workflow efficiency, and the learning curve associated with robotic techniques. The publications demonstrate a consistent trajectory toward establishing evidence-based standards for robotic surgical training and performance assessment.

Dr. Oh is a strong advocate for multidisciplinary care, working closely with colleagues in gastroenterology, pulmonology, medical oncology, radiation oncology, radiology, and pathology to provide comprehensive, patient-tailored treatment. His clinical philosophy emphasizes a team-based approach to achieve optimal outcomes for patients with complex thoracic conditions.