Don’t expect robot surgeons, expect a surgeon-AI partnership
Artificial Intelligence (AI) is prevalent across every industry. Within general surgery, AI is a notable prospect. AI-assisted surgery may be in its infancy, but it is at the forefront and has demonstrated exceptional potential within the space. Using AI to determine patterns, robotic surgery can assist with surgical procedures to improve best practices. Real-time instances exist within robotic surgery.
Very few robotic surgical systems are available on the market. Some are just an extension of the surgeon without AI support, while others use AI to a larger extent. There is huge potential for these technologies to combine together to develop an effective solution.
Da Vinci System: Receiving FDA approval in 2000, the da Vinci Surgical System is manufactured by Intuitive Surgical. It is at the very forefront of minimally invasive procedures and has no real competitor on the market. Intuitive Surgical is working to enhance its AI technology and is moving towards the completion of general repetitive tasks being completed wholly with AI.
Virtual Incision: Virtual Incision developed an advanced miniature robot that focuses entirely on MIS, thus allowing for microscopic surgical procedures to occur. The company is partnered with NASA, and its robot that can slip through a belly button in order to complete procedures and save space is currently being tested on animals.
Mazor Robotics Renaissance: The Renaissance System focuses on spinal procedures. The system enables precision with MIS procedures. Neurosurgeons and orthopedic surgeons can improve patient procedures by using the specialized robotic surgical system. Medtronic bought Mazor for $1.7B.
Monarch Platform: Johnson and Johnson acquired Auris for $13.4B on February 13th. Auris’ focus is on lung cancer diagnosis and treatment. With the use of endoscopic abilities, however, the system will handle many diseases in the future.
AI can be split into smaller segments which alone can provide big opportunities for AI within healthcare and surgery. Using all these segments together will be the innovation that will leave an everlasting mark on the Information Age.
Machine Learning: is the ability of a machine to learn via patterns and go on to make forecasts on what it has learned. There is unsupervised learning, which gives the machine the ability to learn from the patterns by itself, and there is supervised learning, where partial labelling of data is performed.
Artificial Neural Networks: are made of various computational units that mimic the biological nervous systems. It has layers of input and output units.
Natural Language Processing: is the system having the ability to discover and understand human languages. Syntax and semantics have to be taken into account, not just basic word recognition.
Computer Vision: is the ability of a machine to understand videos and images.
Presently, NLP is the driving force behind the use of AI in healthcare. It aids the Electronic Health Record (EHR) analysis and management. Some areas have had more associated R&D. In the same way, Computer Vision is a leader in analyzing scans to determine cancerous cases, and uses laparoscopic video to cover gastrectomy procedures, yielding 92.8% accuracy including uncovering any missing or unexpected steps. Every AI segment can be used entirely to generate holistic solutions for both practitioners and patients.
When it comes to the era of intelligent automation, the collaboration of robotic surgery systems and AI is inevitable. It will allow for better patient outcomes through accuracy. Tech giants like IBM and Google have already created AI models that can be layered into many systems. With companies like Johnson and Johnson and Medtronic, market leaders in medtech continue to acquire companies to move into this field. The future looks good. The aim is to support existing procedures and remove the unavoidable human mistakes by using robotic surgical assistants. Surgeons need to allow integration of AI layered robotic surgery assistants into their workflow, as ultimately, this will allow for better patient outcomes.