This is actually happening now with the help of Artificial Intelligence (AI). The University of Tokyo recently reported that Watson, IBM’s cognitive supercomputer, correctly diagnosed a rare form of leukemia in a 60-year-old woman. Doctors originally thought the woman had acute myeloid leukemia, but after examining 20 million cancer research papers in 10 minutes, Watson was able to correctly determine the actual disease and recommend a personalized treatment plan. AI – and its related applications, – are changing healthcare as we know it. The advancements made in AI will revolutionize research and, ultimately, personalized medicine.
Big Data has been a buzz word for several years now. Hospitals, like enterprises, have been drowning in big data. From the moment doctors begin keeping patient records, they – and now hospitals – have been amassing large quantities of complex data within patient medical records; including handwritten notes, X-ray results, blood samples, vital signs, DNA sequences, and more. Historically, this data has been disparate and existed in hard copies only, making it nearly impossible to analyze in aggregate. Now with AI, analytic tools and other technological advancements, there is a way to actually organize, analyze and cross reference the data, enabling hospitals, doctors and researchers to finally put that data to use.
AI is impacting nearly every aspect of the healthcare industry from patient care such as the examples described herein to hospital security and pharmaceutical drug development (stay tuned for a future post on how AI may just be the solution to rising drug prices). Mellanox is committed to the cause and is helping to accelerate many of the world’s leading AI, ML and DL systems with solutions like RDMA, GPUDirect RDMA, Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)™ and intelligent interconnects that are able to handle the highest rates of real-time data and mitigate network congestion.
While there is generally a solution to any problem, often times it isn’t that we can’t see the solution, it’s that we can’t correctly identify the problem. AI is able to learn from each piece of data it is given and rapidly re-evaluate its analysis as more data and more is received. This enables doctors and researchers to better identify problems and, subsequently, the potential solutions to those problems. A door to a world of possibilities has now been opened, and with it, the potential to find cures for the thus-far incurable diseases, perhaps even within our lifetime.
AI is not limited to traditional data on a spread sheet. It can interpret and aggregate imaging, text, handwritten notes, test results, sensor data, and even demographic and geo-spatial data. AI will be able to cross reference data, find commonalities and draw insights that were previously impossible due to data silos or the sheer amount of time it would take for a human to crunch the numbers. It can also consider seemingly unrelated or outside factors that doctors and researchers may not immediate see as relevant. For example, environmental factor, such as elevation, humidity and proximity to certain dense mineral deposits, factories or agriculture. This ability to rapidly analyze data, and potential correlations, creates a more comprehensive and holistic view into a patient’s health.