Please ensure Javascript is enabled for purposes of website accessibility

Robotic pancreatic surgery

An established approach

In robotic surgery, the robot is always an auxiliary instrument that enables high-precision surgery with small incisions. In this process, the movements of the instruments are always controlled by the surgeon, who sits next to the patient and works with a 10x magnified, three-dimensional view of the surgical camera. Another assistant is directly sterile on the patient at all times, where he also monitors and controls the movements and operation of the robot.

Our vision is to continuously optimize robot-assisted surgery in the field of the pancreas and to provide patients with a gentler and more effective treatment. Robotic-assisted surgery has made significant advances in various surgical fields, including pancreatic surgery. Operations on the pancreas - like many other operations - are suitable for keyhole surgery (so-called minimally invasive or laparoscopic surgery). At present, robotic assistance is also used in Heidelberg for larger operations (e.g. "Whipple operation") on the pancreas, and robotic pancreatic surgery is now our standard for small operations. At Heidelberg University Hospital, we offer this surgical procedure especially for benign changes in the pancreas (cystic tumors, endocrine tumors, chronic pancreatitis).

Finally, minimally invasive surgery (MIS) is not a new invention and follows the principles of open surgery. MIS techniques, such as laparoscopy, were developed to reduce the invasiveness of surgical procedures; surgery is performed through four to five small incisions using a camera. The advantages over traditional surgical procedures are primarily better cosmetic outcomes and often less need for pain medication after surgery.

In addition, AI and Big Data analytics are already making an important contribution to the field of surgery. AI algorithms can support various aspects of surgical care, including preoperative planning, intraoperative guidance, and postoperative decision making. Machine learning models can analyze large data sets to identify patterns, predict outcomes, and optimize treatment strategies. AI-powered systems can help surgeons interpret medical images, provide real-time feedback during surgery, and improve patient safety. Efforts are underway to improve data collection and interoperability and to address issues related to privacy, standardization, and integration of different healthcare systems. Development and research in this area has a bright future.

This approach typically results in less trauma, shorter hospital stays, and faster recovery times compared to established open surgery.


Author: Benedict Kinny-Koester, M.D.