Medical robotics first entered general surgery within the Eighties with laparoscopic tools that enabled minimally invasive procedures, reducing incision size and recovery time. These early systems prolonged surgeons’ capabilities, transforming the surgical landscape.
Today, artificial intelligence (AI) is ushering in a brand new era of precision and control within the operating room. Yet despite this progress, robotic systems remain limited to pick procedures, leaving most surgeries depending on traditional methods — and lots of patients without the advantages of enhanced consistency and outcomes.
As medical technology continues to evolve, how can AI applications in surgical robotics scale to rework healthcare on a broader level?
Increased Market Potential
Fueled by increased robotic VC funding and the digital transformation of the last five years, the robotics industry is seeing fast-tracked market results with no signs of stopping. Earlier this yr, Nvidia announced its intent to extend investments in its robot development, signaling a positive shift for the long run of robotics. Similar investments in robotics by large-scale players will further advance robotic technology through data collection and machine learning, while providing additional resources and insights.
Surgical robotics industry leaders, corresponding to Intuitive Surgical, Medtronic, and Stryker, have pioneered robotic-assisted surgeries for various procedures. Since introducing its da Vinci system for general surgery in 2000, Intuitive Surgical has continued to iterate its robotic platform to expand its offerings to cardiac, bariatric, gynecology, and thoracic surgeries, amongst others. With the mass adoption of robotic-assisted surgeries, surgical robotics have consistently been adopted at a faster scale. Between 2012 and 2018 alone, robotic-assisted procedures rose 738% normally surgery.
Looking ahead, surgical robotics have even greater market potential, and are predicted to grow to over $14 billion by 2026 – up from just over $10 billion in 2023. This is especially resulting from greater access to robotic surgery procedures, advancements in automation and digital technologies, and latest players who aim to deliver cutting-edge medical solutions that harness the ability of AI.
Deep Tech Approach
Built on the intersection of disciplines, deep tech merges multidisciplinary technologies corresponding to AI, quantum computing, biotechnology, and robotics to usher in a brand new era of technology. Startups embracing a deep tech approach in robotic surgery are creating revolutionary solutions for the long run, as may be seen in healthtech development, which may improve patient access to critical medical care. With deep tech development, surgical procedures may grow to be fully automated down the road, requiring minimal surgeon assistance and significantly expanding access to treatment.
Emerging deep tech technologies in surgical robotics could make an enduring global impact. With roughly two-thirds of the worldwide population – 5 billion people – lacking access to surgical operation, these latest modalities, powered by AI, can expand general access and shut the surgical care gap.
Fusing AI and Surgical Robotics
AI has innovated and altered how we interact with different technologies and one another. Over the past five years, the transformation brought on by AI has accelerated the event of robotics and created additional applications for AI inside different modalities, including robotic surgery.
Listed here are three essential ways AI is making a quick and profound impact:
1. Embodied AI
Technology is changing how we interact with the environment and the people around us. Embodied AI, which incorporates autonomous vehicles and humanoid robots, is the fusion of AI with physical systems to execute complex tasks in real-world settings. When embodied AI is applied to surgical robotics, it has the potential to have long-lasting impacts on enhancing surgical care and improving existing techniques. Nonetheless, embodied AI requires significant real-world data to develop training simulation models, that are used to coach and expand AI capabilities and improve data-driven insights. Until recently, access to large amounts of coaching data has been somewhat limited; nonetheless, because the industry continues to speculate within the training and development of AI models, the simulated data pools are growing at a quicker pace and improving embodied AI functionality.
2. Continuous Data Insights and Guidance
AI-based systems can absorb and comprehend large swaths of knowledge in seconds – much faster than the human brain. By training machines on large data sets, data-driven insights can inform surgical decisions before surgeons even set foot within the OR. AI-driven training simulations can significantly profit surgeons, as training on data sets which might be based on 1000’s of surgeries provide surgeons with trends and techniques to deliver a greater patient experience, and likewise allow them to organize for and understand the intricacies of rare or complex cases before they face them within the OR. This process can significantly speed up and shorten the long learning curve surgeons face when training to achieve peak surgical performance.
When applied to real-time imaging and visualization technologies, AI-driven data can even enhance surgeons’ decision-making capabilities during operations. By providing surgeons with insights to regulate surgical plans during procedures, AI-based systems can empower surgeons to optimize techniques and approaches in real-time. Through AI-driven imaging systems, surgeons can receive advanced imaging analytics and real-time 3D “maps” of the surgical sites. These augmented overlays may give surgeons expanded insights into the surgical field alongside real-time feedback on their surgical techniques. Robotic surgery platforms are on the forefront of integrating this technology into the OR, with the goal of accelerating surgical precision and outcomes.
Moreover, by providing ongoing feedback post-operation, AI-based systems can provide worthwhile feedback to surgeons about their performances during procedures – highlighting weaknesses and strengths, and suggesting specific strategies on the right way to improve them. Such platforms can even recommend latest treatment plans based on each patient’s history and the actual procedure’s data evaluation, and empower surgeons with additional information that may enhance further treatment. As such, AI platforms have the potential to soak up and adapt surgical feedback throughout the complete surgical cycle (before, during, and after) through an AI feedback loop to extend surgeons’ precision and performance.
3. Increased Accuracy and Precision
Individual surgical skills often vary amongst surgeons resulting from their access to top-tier opportunities, from program location to surgical mentorship access. For example, the sector of ophthalmology has a steep learning curve. On average, it takes no less than 15 years of coaching and surgical experience to achieve peak performance as an ophthalmic surgeon. With a growing aging population and a dwindling variety of surgeons, a brand new solution is required to cut back the surgeon’s training period and standardize the accuracy and precision of look after all.
Along with reducing the training curve for surgeons and allowing them to achieve peak performance faster, introducing AI-based platforms into the surgical process can increase accuracy and precision and will improve suboptimal outcomes. Semi-autonomous and increasingly autonomous features in robotic platforms can eliminate the surgeon’s natural hand tremor and improve overall precision and accuracy, thus improving clinical outcomes. As well as, the power of AI-based systems to acknowledge unique anatomical structures and supply the precise location for incisions and other surgical steps – especially in complex procedures or anatomical areas – can significantly reduce the speed of surgeon errors by improving spatial awareness of anatomical structures. As such, all surgeons utilizing AI-based systems will have the ability to offer consistently more precise care.
When incorporated into the surgical process, AI-based robotic platforms provide invaluable insights that may enhance the general experience for each the patient and the surgeon.
Conclusion
AI will proceed to play a big role in advancing healthcare in the long run. Incorporating advanced AI technologies into our healthcare services, corresponding to electronic filing, diagnostics, and health monitoring and tracking, in addition to surgical care, is imperative. In doing so, we will improve the general patient and surgeon experience.
In robotic surgery, AI is expediting the technology’s transformation and patient access to consistent, high-tier treatment. Advancements in robotics, coupled with AI and automation, will proceed to usher in latest applications, creating a better level of standardized care and launching healthcare quality and access to latest heights.
