UCSF surgeons spotlight innovation, perioperative research at national surgical conference

Editor's Note

University of California San Francisco (UCSF) surgeons and researchers presented a wide range of original work at the American College of Surgeons’ 2025 Clinical Congress in Chicago, held October 4–7. According to an October 7 article published by UCSF, the meeting featured topics from perioperative opioid stewardship and imaging practices to robotics, artificial intelligence, and complex cancer management.

Julie Ann Sosa, MD, MA, FACS, chair of the UCSF Department of Surgery, discussed the newly released 2025 American Thyroid Association guidelines for managing differentiated thyroid cancer. Dr Sosa co-chaired the task force that developed evidence-based recommendations covering diagnosis, staging, treatment, monitoring, and survivorship.

In hepatobiliary surgery, Carlos Corvera, MD, FACS, presented a robotic-assisted completion cholecystectomy case using fluorescent cholangiography for improved visualization during common bile duct exploration. The case, led by first author and surgical resident Fernanda Romero Hernandez, MD, demonstrated advanced use of fluorescence technology to enhance safety and efficiency in complex procedures.

Several presentations centered on perioperative process improvement. Medical student Shraddha Pandey reported results from a pilot of a clinical decision support tool that integrates opioid use data into discharge prescribing. Despite persistent over-prescription, follow-up interviews identified barriers such as workflow integration and physician adoption, showing the importance of training and system alignment.

In trauma and critical care, resident Andrea Gochi, MD, presented data questioning routine MRI use for trauma patients with neck pain but negative CT scans, finding that MRI rarely changes management. The research supports more selective imaging to reduce unnecessary testing and cost.

Artificial intelligence also featured prominently. Postdoctoral scholar Amir Ashraf Ganjouei, MD, MPH, described a machine learning model that predicts optimal length of stay following elective colectomy, showing strong performance and potential to guide personalized perioperative planning.

Other UCSF researchers explored 3D modeling in pediatric urologic reconstruction, evolving trends in aortic valve replacement, equity in orthopedic outcome reporting, and team dynamics affecting OR efficiency.