Samuel Szomstein

About

Samuel Szomstein is a leading figure in metabolic and bariatric surgery, with a research focus on surgical innovation, artificial intelligence integration, and outcomes optimization. His major contributions include shaping international consensus on AI’s role in bariatric procedures, where his 2025 expert consensus paper (14 citations) outlines how machine learning and deep learning are revolutionizing surgical science and practice. Szomstein also advances comparative effectiveness research, as demonstrated by his 2024 propensity score–matched analysis of over 8,000 patients from the MBSAQIP database, which critically evaluates laparoscopic versus robotic-assisted bariatric surgery and the persistent challenges of the learning curve. His work has garnered significant attention, with his most cited papers collectively influencing clinical guidelines and surgical decision-making. Szomstein’s research bridges cutting-edge technology and practical surgical outcomes, making him a key voice in the future of minimally invasive metabolic surgery. His contributions are essential reading for surgeons and researchers seeking evidence-based insights into AI applications and robotic surgery’s evolving role in bariatric care.

Research Focus

Key Achievements

Top Papers

1. 1
   International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery

   14 citations · 2025
2. 2
   Laparoscopic versus robotic-assisted primary bariatric-metabolic surgery. Are we still expecting to overcome the learning curve? A propensity score–matched analysis of the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program database

   8 citations · 2024

Key Collaborators

Contact & Links