Imagine reshaping the surgical landscape with independent surgery

A star was born in the famous AI surgical laboratory in Baltimore, Maryland. Axel Krieger of Johns Hopkins University and colleagues bring intelligent organizational autonomous robots into artificial life. Stars can perform complex keyhole anastomosis surgery with minimal human intervention, helping to overcome the challenges of these delicate procedures, during which two small intestines are sewn in succession.

According to Krieger, robots combine novel suture tools, imaging systems, machine learning algorithms and robotic controls. Star is not about replacing human surgeons – it aims to incorporate it into the surgical workflow, thereby enhancing surgical consistency from patient to patient. Currently, this is an impressive research project, but it illustrates the direction of travel.

Fast forward ten years and imagined under the supervision and guidance of a surgeon that autonomous surgery can be planned and completed based on an individual’s pathology and needs. Robots can modify plans in real time, respond to complications and optimize results. However, under human supervision, the star constellations are very different in a world in a world that is very different from the current surgical model. And if the expected time range is even vaguely real, then this is the world we start preparing for today.

AI-enhancing shrinking timeline

Autonomous surgical robots are expected to improve consistency, patient outcomes and obtain standardized surgical techniques. By 2033, the global market for automated surgical robotics is expected to reach US$11.07 billion. Although extensive autonomous surgery (especially soft tissue surgery) is farther away, it is not as far away as some people think. Over the past three decades, researchers and developers have taken temporary steps by more predictable environments in orthopedics. But over the past few years, the rapid maturity of AI solutions has moved the dial from daily AI applications to self-learning systems. These technologies, along with a strong communication network, are long-term drivers of automated surgery. For example, Large Language Models (LLMS) can now view video content, draw inferences, and copy human behavior.

Knowledge, tools and confidence already exist to support autonomous systems at scale. But as the expected time frame for autonomous surgery narrows daily, healthcare and MedTech organizations are prompted to consider entirely new directions.

A world that requires the creation of a harmonious execution of automated procedures with human surgeons requires critical structural, framework and incremental steps. And if the timeline is 10 to 20 years instead of 50 years, then today’s decision makers have basic considerations. What will happen next at the forefront of so many transformative changes? What profound impact will an autonomous system have? How does the global health care landscape prepare for the future of automated surgery?

Understand the impact of autonomous surgery

Research shows that robots can perform surgical tasks 50 times faster than human surgeons. If a single robot can do the work of 50 surgeons, human professionals can be released to focus on critical event resolution, the most complex procedures, and accelerate training and capabilities around the world.

Faster and more accurate surgery can improve the accessibility of half of the world currently lacking consistent quality healthcare. Automated solutions can take stretching surgery to a whole new level, with the help of lightning-like global communication, so human professionals don’t have to appear physically with the system.

Process innovation is another advantage. Most surgical instruments are designed for use by humans with two arms, two hands and two eyes. But autonomous systems can use multiple tools to complete several tasks immediately, greatly changing the nature of the program. For example, while removing a piece of cancerous tissue, surgeons are currently using a staple device, which secures two straight metal staples to help close the resection. If an autonomous robot is released from the limitations of human control, it is possible that metal staples (possibly tear) replaced by a soluble suture, i.e., the braided suture in situ. Like an apron, cutting profiles can follow the shape of the tissue, thus supporting faster and more efficient procedures.

All these changes affect cost dynamics. For example, if a robot can perform faster, streamlined surgery without extensive human guidance, as surgeons will redeploy and increase the number, the balance of resources will change. Do robot providers charge the same fees as consultants, or does a competitive market dramatically reduce program costs? If you do this, the decisions about whether and when to operate will change as well. This can open the door to earlier and wider interventions.

Regulatory landscape

While this future is promising, autonomous surgical tools require new guardrails and careful supervision. The autonomous future requires the establishment of a regulatory approval process to adapt to iterative, nearly continuous learning. Suppose surgical tools use black box AI products to collect sensitive data during the process and then apply insight without explaining how. If the error hurts the patient, it is impossible to track how or why. Not only that, but if insights are provided for the shared database, it may be multiplied by an error.

Currently, regulators check for updates to the system before release. However, in the world of continuous learning machines, a new model is needed. The guiding principles for the latest release around Good Machine Learning Practice (GMLP) are just the beginning. Imagine the “dynamic verification system” of the AI ​​platform that checks whether the insight is safe and worth implementing. If it is not an hourly verification cycle, requiring new processes, deeper integration or even new management functions, you need to continue daily data harvesting. The CIO may need to work hand in hand with the CTO to play an important gatekeeper role and establish close partnerships with regulators.

Draw an autonomous future

There are many smaller steps from now to a completely autonomous future, but if the organization does not plan this situation, is it possible that they are being disturbed by other players? Stop the gap solution is important, but if fully autonomous surgery is one to twenty years away, is it right to invest in a system that may be outdated within a few years? How do R&D decision makers place the right bets and how do they ensure their organization is ready?

To prepare for the future of autonomous surgery, MedTech leaders should explore the actual implications of potential scenarios and build investments accordingly. The key is to develop a living roadmap that will drive decisions day by day with the help of macro and micro trends. Performing these assessments as part of the day’s work will demonstrate the actions required to prepare for a variety of possible futures. With a roadmap, companies can monitor, update and optimize their strategies.

In terms of autonomous surgery, a crucial early action is exploring how AI can be leveraged and provided with relevant data from different parts of the care pathway. Surgeons not only respond to what they see, but also inform them through the patient’s history and personal experience. Accessing and understanding the relevance of this data ecosystem will help build effective models that provide context for the decision engine that ultimately controls the outcome. Armed data, digital twins can be used to predict how the system will respond and help optimize algorithms offline in a secure environment.

These basic steps need to be linked in the ecosystem of joint stakeholders. MedTech has partnered with key technology players, while others are actively promoting the healthcare sector in the healthcare industry in the healthcare AI accelerator program. The wave of activity will continue.

By converging horizon scanning and road mapping, strategists in MedTech and healthcare sectors can prepare for tomorrow’s “reverse ripple effect” of changes in today’s decisions. These approaches should be as critical to the company’s operations as monthly accounts, especially given the speed of technology development.

Editor’s Note: neither the author nor his company belongs to any of the entities mentioned in this article.

Photo: Gorodenkoff, Getty Images

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