The mention of Artificial Intelligence (AI) is ubiquitous across all industries; however with sci-fi aspirations for AI there are only a few tangible examples of the tech within given industries. A noteworthy prospect is the use of AI within general surgery. At the forefront is AI assisted surgery, albeit in its infancy, demonstrates huge potential within the space. While current robotic surgery utilises AI to determine patterns within surgical procedures to improve best practices, there are instances of real time AI within robotic surgery.
There are a few robotic surgical systems on the market, some use AI while others are just an extension of the surgeon without AI support. The potential for these technologies to merge together and create consolidating effective solutions is immense.
Da Vinci System: Manufactured by Intuitive Surgical, the da Vinci Surgical System received FDA approval in 2000. It has been at the forefront of complex minimally invasive surgeries with no real contender within the current market. The company is in works to improve its AI technology and move towards completion of mundane repetitive tasks autonomously with AI.
Virtual Incision: Focusing on MIS, Virtual Incision created an advanced miniaturized robot which allows for microscopic surgical interventions. In partnership with NASA, the robot can be slipped through the belly button in order to save space and complete procedures. (Currently being clinically tested on animals)
Mazor Robotics Renaissance: Focusing on spinal procedures, the Renaissance System allows precision within MIS surgeries. This is a specialized robotic surgical system which enables neurosurgeons and orthopedic surgeons to improve patient procedures. Mazor was purchased by Medtronic for $1.7B.
Monarch Platform: Auris, which was acquired by Johnson and Johnson on February 13th for $3.4B focuses on lung cancer diagnostics and treatments. However with endoscopic capabilities the system will be able to tackle a wide array of diseases in the near future.
AI can be broken down further into segments; these segments alone provide great opportunity for AI within surgery and healthcare. However the use of all these segments together will bring forth the innovation that will leave a lasting mark from the Information Age.
Machine Learning: the ability of a machine to learn through patterns and in turn make predictions on what it has learned. There is supervised learning, where there is partial labelling of data to learn. Unsupervised learning is the ability of the machine to learn from patterns within the data itself.
Artificial Neural Networks: Mimicking biological nervous systems the neural network is composed of numerous computational units which have layers of input units and output units.
Natural Language Processing: The ability to detect and understand human languages. Semantics and syntax need to be taken into account, not just simple word recognition.
Computer Vision: The ability of a machine to understand images and videos.
AI is already being utilized within specific context and some areas have had more R&D associated with them, currently the NLP is at the forefront of AI use within the healthcare system and aids in Electronic Health Record (EHR) analysis and management. Similarly, Computer Vision as at the forefront of analyzing scans to detect cancerous cases, moreover laparoscopic video analysis of sleeve gastrectomy procedures yielded 92.8% accuracy in identification of steps including any missing or unexpected steps. Each segment of AI tech can be used cohesively to create holistic solutions for patients and practitioners alike.
The marriage of AI and robotic surgical systems is inevitable, with it comes the advent of intelligent automation which will allow better patient outcomes through precision. Conventional tech giants such as Google and IBM already have developed AI modalities that can be layered onto any system, in this case robotic surgical systems. As medtech market leaders such as Medtronic and Johnson and Johnson acquire companies to step into the space, the future is bright. The goal is not to create robotic surgical systems that replace surgeons but ones that augment procedures and remove unavoidable human attributes such as surgeon tremors. Surgeons need to integrate AI layered robotic surgical systems into their workflow, which ultimately will produce better patient outcomes.