From the April 2019 issue of HealthCare Business News magazine
How are innovators addressing these challenges? None of these are new, and the drive to provide better care to more patients at lower costs has spurred innovations that are making a big difference. But today, the state of the art has matured to the point where we can expect these forward steps to become leaps. In particular, I believe that the three technologies to keep watching are: big data and machine learning, advanced imaging modalities, and autonomous surgery. There are overlapping reasons why these three are positioned to bring about this revolution.
Big data is the missing link between today’s MIS practice and the ability to truly democratize it and bring about the ability to provide care to as many patients as possible. Because of the high levels of skills, training, and experience required, there has long existed a chasm of ability between high-volume practitioners and centers and their lower-volume counterparts. As a result, depending on geography, socioeconomic status, and type of health insurance, many patients today effectively lack access to the best form of surgical therapy – often MIS.
Convergence of the maturity of machine learning algorithms, widespread ability to store and efficiently access huge amounts of data and processing power, and growing high-bandwidth low-latency data networks will allow computers to bolster human skill and intuition like never before. A highly skilled high-volume clinician today achieves excellent results in large part thanks to having thousands of cases of experience to draw upon. When an AI-based assistant is able to provide decision support based on tens of thousands, or millions, of cases, the chasm will narrow and the quality of MIS will improve across the board.
Advanced imaging is an area of highly active research. We are starting to see not only the growth of alternate modalities themselves, such as optical coherence tomography and ultra wideband radar, but also the advent of sophisticated image processing capable of generating images that are much more readily interpreted than before. As imaging innovation continues, we will see a continued reduction in unintended damage and an increased ability for clinicians to intuitively appreciate the structures being navigated and treated.
In autonomous surgery, or semi-autonomous surgery, robots provide assistance to surgeons. Existing surgical robots already give surgeons more control and higher precision than they might be able to achieve using their hands and passive tools. New innovations combining robotics with machine learning and advanced imaging will bring this to the next level, allowing robots to handle many parts of surgical procedures. In essence, there will be a division of labor in which the surgeon gives the robot high-level instructions about how to obtain access and positioning for the procedure at hand. Once the appropriate instruments are positioned in precisely the correct way autonomously, the surgeon will be able to take over knowing that they are poised for optimal performance.
