Over the past decade, the healthcare industry has witnessed the incredible power of technology to drive innovation. Spurred on by the pandemic, new ways of working have taken hold while the pace of technology adoption has accelerated – notably artificial intelligence. One of the most exciting new use cases is in one of the most iconic places in healthcare: operating rooms.
While we are only at the beginning of the transition of surgery from the physical world to the digital world, we are beginning to glimpse what the future of surgery could look like. However, to make it a reality, a crucial step remains to be taken: demonstrating how this technology brings value and better clinical results.
AI in action: innovation first
AI still has a long way to go before it reaches its full potential in healthcare. Previous examples of AI being used in surgery have focused primarily on the use of AI in cameras to stream live video data.
However, recently, the British startup Hypervision used AI in an in-vivo surgery study at the IHU in Strasbourg, France, thus taking several steps ahead of what was done so far. The deployment of Hypervision has augmented the physician’s vision to create a heat map alongside conventional information to show an intuitive display of tissue analytic information that was previously invisible to the human eye.
This helped provide additional information for surgical decision-making during the procedure itself, while reducing the risk of complications for patients during the procedure.
For young companies in the sector to seize commercial and research opportunities, they must prove the legitimacy of their work through concrete clinical results. This involves contributing to the scientific literature and obtaining feedback from the clinical community, whether positive or negative. This is how healthcare start-ups develop a solid clinical strategy, as well as a business plan and set of technologies.
In turn, large organizations and established institutions such as King’s College London, for example, benefit from privileged relationships with young companies by accompanying them as mentors or partners. For KCL, experts can offer guidance to start-ups while giving students the opportunity to see how their work translates into real-world applications, which supports their learning and development. It is also very useful to stimulate competition, which will lead to increased investment in the sector, demand from start-ups and adoption of technologies by the clinical community.
Large-scale benefits for patients
If we look to the future of healthcare, it is important to consider the basic use cases that AI and robotics can accomplish in the near future. The pandemic has not only shown us how quickly healthcare systems can be challenged, but also the persistent challenges to data acquisition, quality and sovereignty, which remain to this day.
Geographical interoperability also poses problems. But whether it’s data sharing across borders, hesitation around AI or the technology divide, with these obstacles come opportunities. Now more than ever, conversations about realizing those opportunities need to happen to level the playing field.
The true value of AI to some healthcare professionals and the role it will play in the future of clinical care is still unknown. To begin to answer this question, we need technology, techniques, and resources to be directed toward high-value clinical and financial use cases. For example, we are now seeing AI move from radiology to pathology and other areas of microscopy because that is where it brings the most value. Other examples of this phenomenon appear in the world of genomic sequencing, where the adoption of AI is accelerating. The same trend is seen in the prediction of protein folding, which is becoming more efficient thanks to AI.
The history of AI cannot be siled between different healthcare sectors. It must be understood holistically, across the entire patient journey. How to improve the treatment and outcomes of a stroke patient with imaging? How can AI be used to better understand genetics and analyze unstructured data? An interdisciplinary and collaborative approach is crucial. We – academics, startups and tech heavyweights – need to work together to make a difference.
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Healthcare: will AI take center stage in operating theatres? – AI news
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