Robotic Surgery and AI

More than 5,000 surgical robots were used in more than 1 million procedures worldwide in the last year as Roger Smith said. Countries are using these robots for some procedures like: orthopedics, urology, general surgery, gynecology, neurology, thoracic, otolaryngology, bariatric, rectal and colon, multiple oncologies – even dental implants and hair transplants. Robotic surgery is no longer seen as a technology of the future – it’s an active and effective technology of today.

 Nowadays Artificial intelligence is being applied to surgical robotics. Countries and Manufacturers see the need to use deep learning data to automate rather than behavior programmed by an engineer that does not know all the scenarios. This deep machine learning data is collected from watching surgeons perform from the Operating Room (OR). These robotic technology platforms can create an interaction between humans and machines.  This process called cybernetics that will deliver innovation through efficient, sustainable and cost-effective products, services and work environments.

Surgeons are able to move the camera simply by moving their eyes and thanks to this data and complex algorithms, AI can determine patterns within surgical procedures to improve best practices and to improve a surgical robots’ control efficiency. In health and medical area, AI is also being used with machine vision to analyze scans and detect cancerous cases. Laparoscopic video analysis of surgeries, helps to identify missing or unexpected steps in real time.

Robots can repeat exact motions due to their superhuman abilities. Sometimes in delicate areas, surgeons need extremely steady hands when they are working. For example, in eye surgery Tests of a system to remove membranes from patients’ eyes have been successful and, in some cases, the surgery via the robotic system was more effective than doing it manually directly by a surgeon.

Via Deep Learning and artificial intelligence, robots can use data from past operations to inform new surgical techniques. On the other hand, Robot-assisted surgery is considered “minimally invasive” because patients won’t need to heal from large incisions.

AI, Machine Learning and specially Deep Learning support human providers to provide faster service, diagnose issues and analyse data to identify trends or genetic information that would predispose someone to a particular disease. When by saving minutes surgeons can saving lives, AI and machine learning can be transformative not only for healthcare but for every single patient. The combination of AI with surgical robotics may lead countries to permit the augmentation of surgical capability to optimize outcomes and increase access to care.



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