Intuitive Surgical shipped their first surgical Robotic Assisted Surgery (RAS) in 1998. In the twenty-five years since, the technology has become ingrained in most surgical procedures. Incredibly, the market itself is projected to grow 7-fold over the next 7 years. This growth has created a crowded market with over 125 companies entering the RAS market in recent years. Most of these companies are focused on developing surgical robots aimed at different specialties (orthopedic, gynecologic, cardiac, thoracic) and surgical approaches (laparoscopic, endoscopic), and each is in a time-to-market race to develop capable, reliable, safe and cost-effective products. Below we examine some of the challenges ahead for RAS system developers and offer ideas to avoid the pitfalls.

 

Architecture Planning

 

Increasing the automation of surgical robots represents a real challenge as they are at the pinnacle of complexity, both mechanical and software, for medical devices. The software systems to drive these products are measured in millions of lines of code and take years and tens of millions of dollars to develop and test. Efficiency in architecture planning and development is required to meet the market demands within the available market window and available capital.

 

Use of formalized techniques, such as Architecture Trade-off Analysis Method (ATAM), to objectively evaluate the impact of specific software architectural decisions can generate outsized benefits:

 

• Reduction of unnecessary design complexity
• Improved design clarity
• Better alignment to longer-term product goals
• Increased reliability
• Easier module upgrades and software maintenance. 

 

ATAM drives a process that forces deeper thinking of trade-offs within system and software architectures, uncovering hidden conflicts that may limit overall system performance long after the decision is made. This method of planning allows engineering and product management to participate in a thorough evaluation of trade-offs and that evaluation results in a true alignment between the two groups.  That alignment pays dividends throughout the development process, avoiding costly delays due to late architectural changes, performance issues, and verification shortfalls. All of these are major drivers of both initial development costs and life cycle product support costs. 

 

Accelerate Procedure Set-up Time

Currently, the cost of RAS procedures is impacted by the readiness of the robot system. Many RAS systems, even those with high levels of reliability, have significant time lost in movement and re-calibration. The emerging trend for mobile, rapid setup RAS systems has the potential to both lower the procedure price point and further expand the potential markets. Efforts focused on increasing the automation and speed of recalibration requires a rethink of design approaches from current systems that are designed to be used primarily in fixed locations.

 

Utilizing techniques from other precision automation and control industries, such as semiconductor or manufacturing equipment, can open interesting pathways for the integration of sensor technology that can dramatically accelerate the process of calibration, lowering the costs of intervention by technicians and freeing more time for the revenue producing procedures for which the device is intended. 

 

Incorporating AI

 

Just over the horizon looms the specter of adding AI into RAS systems, providing a path to semi-autonomous systems by the end of this decade. Development of these new AI enabled products must be done without a significant increase in cost in order for the new entrants to survive in the early years of a developing market.

 

Providing architectural support for a rapidly evolving technology such as AI, within a comparatively slowly evolving product such RAS represents a significant challenge. Strong componentization rules are required to ensure that in the future the AI engine can be removed if it is determined that a switch to a new engine is needed. Improper design can lock an application into an early choice, causing both time delays and large cost implications in the future.

 

Cyber Challenges of Remote Monitoring and Proctoring

 

Additionally, one of the biggest cost drivers of fielded RAS systems is training and proctoring of the surgeon operators. While training is primarily an operational issue within the OEM organization, design support for systems extension which support training and, especially, procedure proctoring can result in significant reduction of adoption time and costs associated with new product introductions. Using tight synchronization of remote access, along with all the requisite cyber security issues, is not a trivial problem. Taking the peculiarities of remote access and monitoring into consideration as some of the initial architectural drivers ensures the sensors, controls, and dataflows are planned into the overall designs from the beginning, not welded on at the end.

 

Of course, like any medical device that is exposed to the internet, RAS systems will need to integrate advanced cyber security technology that meets the new FDA guidance.  However, the FDA’s March announcement of its formal adoption of 2022’s cyber security guidelines represents a large step up in the need for security vigilance on the part of all medical OEMs. Even more than requirements of better thought-out architectures and more comprehensive threat modeling, internal processes must be developed to monitor performance in the field, evaluate new releases from utilized third-party software providers, and determine how and whether to integrate and release security patches into fielded products.

 

RAS System Verification Strategy

 

And finally, verification strategies for medical equipment can be challenging in their own right. As mentioned above, RAS systems are on the cutting edge of complexity in the MedTech space. Specific decisions made during the system architecture stage can often have disproportionate impact on testing complexity, time, and costs. 

 

Testing systems of this complexity require sophisticated equipment to inject faults into the system to test error capture and recovery, communication resiliency, and the expected lifecycle of life-limited mechanical components – all on a large scale. Integrating test fixtures, automated testing, simulation, and emulation represents a development effort almost as complex as the product itself. Therefore, verification strategy cannot be left to the later stages. Large distributed systems such as RAS can benefit enormously by adopting and adapting some of the technology and techniques developed in DevOps for cloud systems. These tools provide sophisticated structures for the continuous integration and testing of componentized systems in a controlled and efficient manner, significantly shortening time for system testing.

 

Conclusion

 

It is time for OEM’s to take a step back and realize that RAS systems are at the cutting edge of computer and software technology, as well as clinical and therapeutic technology.   The array of development challenges would be daunting to even a large, mature device manufacturer like Medtronic. To the smaller and mid-sized organizations looking to innovate or disrupt the RAS market, the ability to address these issues efficiently and effectively will be the difference between market success and missed dates and market failure.