About Ozanan Meireles, MD

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Dr. Ozanan Meireles is a Bariatric, Foregut, and advanced endoluminal Surgeon at the Massachusetts General Hospital, and an Assistant Professor of Surgery at Harvard Medical School. He is the co-founder and the director of the Surgical Artificial Intelligence and Innovation Laboratory (SAIIL) at the Massachusetts General Hospital.

Dr. Meireles has been in practice for more than 10 years at the Massachusetts General Hospital in Boston, and he is specialized in the treatment of disorders of the Upper Gastrointestinal tract such as Gastro-esophageal Reflux Disease (GERD), Stomach Cancer, Achalasia, Esophageal Swallowing Disorders and Weight Loss Surgery.

Dr. Meireles graduated from the Sao Paulo State University School of Medicine, in Brazil. He completed his surgical training at Michigan State University and his clinical fellowship in Minimally Invasive and Bariatric Surgery at the University of California San Diego. He spent his research fellowship at the Johns Hopkins Hospital investigating augmented reality in surgery and robotic telemanipulation. Furthermore, he was deeply involved in studying and developing Natural Orifices Trans Endoluminal Surgery (NOTES) techniques, and he was among the first to pioneer their clinical applications.

He is a fellow of the American College of Surgeons (ACS) and the American Society for Metabolic and Bariatric Surgery (ASMBS). He serves as the Chairman of the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) Artificial Intelligence Task Force. He also holds key leadership positions and committee memberships in several other national societies in the United States, including the ACS, NOSCAR, ASMBS, and SSAT.

At SAIIL, his research focus is on utilizing computer vision to investigate the intraoperative phase of care through real-time, automated surgical analysis, and provide surgeons with additional data, with the goals of democratization of surgical knowledge, lowering costs, improve patient outcomes, and reduction of morbidity and mortality.

Clinical Interests:




Salem Hospital
81 Highland Ave.
Salem, MA 01923
Phone: 978-741-1200

Mass General Digestive Healthcare Center: Weight Center
50 Staniford St.
4th Floor
Boston, MA 02114
Phone: 617-726-4400

Massachusetts General Hospital
55 Fruit St.
Boston, MA 02114
Phone: 617-643-9841

Medical Education

  • MD, Sao Paulo State University
  • Residency, Michigan State University
  • Residency, Santa Casa De Sao Paulo
  • Fellowship, University of California, San Diego

American Board Certifications

  • Surgery, American Board of Surgery

Accepted Insurance Plans

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Surgical Artificial Intelligence and Innovation Laboratory

Principal Investigator: Ozanan R. Meireles

Our primary emphasis is on utilizing computer vision to investigate the intraoperative phase of care through real-time, automated surgical analysis, empowering Artificial Intelligence to understand what is happening in an operation, reasoning and infer predictions.

We are building technology as the foundation for a worldwide database of surgical cases. A surgeon learns and improves one operation at a time. An AI system can learn from thousands of cases simultaneously. It allows for the collection, analysis and sharing of quantitative evidence in real-time across multiple surgeons -- a “collective surgical consciousness.” The goals of our research in surgery are, the democratize surgical knowledge, lowering costs, improve outcomes, and reduce morbidity and mortality.

Artificial Intelligence for Risk Prediction from Intraoperative Events
This study will utilize our team's previously developed computer vision-based analysis of intraoperative video to integrate quantitative intraoperative data with perioperative data to improve the prediction of patient-specific complications and readmissions for patients undergoing laparoscopic cholecystectomy. Funding: CRICO Risk Management Foundation

Automated Intraoperative POEM Analysis: A Machine Learning Approach
The goal of this study is to develop artificial intelligence to generate compact segmentation and summarization of an endoscopic surgical procedure (per oral endoscopy myotomy) in real-time. This study builds off our initial pilot approach utilizing support vector machines for visual classification in sleeve gastrectomy and pivots to the use of deep learning for our visual model. Funding: Natural Orifice Surgery Consortium for Assessment and Research


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