Radiology is a sector that has operated on enormous data, even before the digitalization. But the space suffers from a key obstacle: manpower. In order to become a radiologist, one has to study and learn the practice for an average of 13 years before being able to treat patients. The amount of knowledge and experience required to properly read and analyze X-Ray, Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) is staggering, while at the same time the responsibility levels are very high and long working hours, the highest for healthcare professionals, are only partly offset by the level of remuneration. The lengthy education followed by intensive working schedule and possibilities for lawsuits dissuade young students from pursuing radiology. This phenomenon is seen worldwide, while simultaneously the demand for radiologists continues to rise. The US expects at least 14 000 new radiologists will be needed to keep up with demand for their services. This increase of 14% would only require for the number of radiologists per 100k population to remain at the current level of 6.3, which is half of what France has at their disposal. The US is only slightly higher than the United Kingdom’s 5.2, which is believed to be a critical number for the efficiency of radiology treatment. And the amount of radiology scans is already believed to be insufficient compared to what would be the optimal. This landscape makes it a perfect place for AI automation technology to be implemented.
Key inefficiencies in current state of radiology
The most precious asset radiologists have is their time. This means that in order to propose any improvement through technology, time savings must be the ultimate result. Any solution that allows for cost reduction with faster analysis time or allows for more revenue due to higher amount of scans analyzed in the same time will be beneficial., The almost endless potential for the increase in radiology scans and thus revenue increase has caught the attention of hospitals and privacy clinic chains.
How Rayscape tackles the challenge
Rayscape offers a platform that integrates directly into the current, conventional workflow of radiologists. This allows for easier adoption and does not require doctors to learn new tools. The company’s product suite CheRayscape offers an analysis possible diseases based on probability, and alerts the attending physicians if there is a need for urgent treatment. Automated highlighting of the most important findings on a scan is highly useful to radiologists especially during long shifts. However the single biggest advantage of Rayscape lies in its ability to analyse the scan in terms of the presence of any anomaly. Since almost a third of scans prove that there were actually no diseases found, this allows radiologists to laser focus on only the most relevant scans. This requires faith and trust of radiologists, andthe key requirement for that is not to deliver a product that second guesses their opinion, but rather saves radiologists’ precious time with top quality recommendations.
bValue invests in Rayscape
We are always interested in real life use cases of artificial intelligence and machine learning, however Rayscape is our first investment in the healthcare segment. What convinced us was the well balanced founding team with high emphasis on technology coupled with market trends and the apparent slow development of the technology by global leaders. Furthermore, we deeply believe in the rapid development of AI/ML technologies around healthcare and radiology specifically, therefore investment in Rayscape provides a unique opportunity to support the implementation of technology in medical healthcare worldwide.