Iris.ai provides multidimensional research for exploring possibilities and limitation in applying Augmented reality to surgical settings
Last week we hosted a Scithon in collaboration with Stryker and the Freiburg University Medical Center. For this Scithon, three teams were trying to find a solution for this research question: What research and development work is necessary to build a “ready to use” adaptive augmented reality (AR) system for surgeons to teach them how to perform a surgical procedure? The judges were looking for the teams to find solutions involving four different dimensions of the question; identifying the capabilities of the technology, outlining the training process for doctors, describing the human interaction and UX and how it would be integrated into the surgical flow. Over the course of the day, we had several conversations with participating doctors and members of the Stryker team which gave us valuable insight and solid background information for the research problem we were trying to solve.
The Scithon’s winning team cited just 13 papers using Iris.ai which allowed them to successfully cover all four of the required angles. The team concluded that the prospect of AR in surgical environments is absolutely feasible however the current technology needs further testing to prove reliability and other improvements to meet surgical requirements. They were able to outline specific uses of the technology and identified that ‘the more rigid and basic the surgical system the easier it is to implement augmented reality applications to improve results.’
The team placing second used traditional research methods to reach their conclusion and while they found more papers, 46 in total, those papers had a much more narrow focus, with most of them targeting a single topic area. The reason being, traditional methods rely on the existing knowledge of the user, where users typically search for more of what they know instead of discovering new, related topics that were previously unknown to them. Their conclusion ascertained that presently there is no technology that encompasses all of the features needed to create an AR system to satisfy all of the requirements named in the problem statement. However, research data and solutions on the key aspects of technical limitations are available and provide the foundation for the development of the envisioned system.
Similar to the winning team, the team that placed third was also using Iris.ai and was able to reach their conclusion using a few number of papers citing 15. While the judges were conflicted on the depth on which they covered the various aspects of the research question, they were able to cover 3 out of 4 of them. Their solution asserted that it is possible to build an AR system comprising of existing surgery navigation platforms, real-time intraoperative imaging and 3D visualization display devices. However, these technologies are not without shortcomings like the need to reconfigure display devices that are too bulky, have limited capabilities and autonomy limitations and improve the tracking of surgical instruments and automatic relevant medical information extraction.
The results of the Scithon spoke to the benefit of Iris.ai’s serendipitous search enabling a multi-faceted approach to research. Whereas traditional search limits the output to singular aspects of the problem, that doesn’t connect all of the possible angles. Current keyword search engines return results that can be biased based on the user’s search history and experience. Iris.ai produces unbiased, objective results creating a multidimensional approach for research discovery. Most research problems require several topic areas to be considered before a solution can be found and Iris.ai enables teams to find the research to cover each and every one of them.
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