Data Diagnosis – I use this term when referring to a medical professional’s diagnosis based on knowledge, experience, and the utilization of big data. Big data, the buzzword lost in the mix of artificial intelligence and machine learning, is vital for the existence of the two. Artificial intelligence and machine learning depend on copious amounts of data. Our world is transitioning from people giving a computer commands or rules to one where people give a computer a problem. Computers use data to generate accurate answers to the problem. Companies are collecting data with or without you knowing, legally of course, to empower their decisions.
When using Google maps, a constant stream of data helps Google improve recommendations used to provide the user with the fastest route to their destination. UPS uses wireless sensors on their trucks which collect billions of data points. These data points allow them to service a specific truck part before it breaks instead of performing inefficient and often unnecessary routine maintenance checks. Modern Medicine, a company that has created a web-based repository of medical information and insights, collects data constantly to help provide doctors with relevant information and insights at their fingertips. These are examples of machine learning.
According to a study conducted by Accenture and Frontier Economics, “The artificial intelligence market is expected to be a $8.3 trillion-dollar industry by 2035 in the United states alone.” Artificial intelligence and machine learning are still in the initial phases of development, and we have barely scratched the surface of what these tools can do. What we do know is artificial intelligence and machine learning algorithms can read thousands of pages of documents (think about medical records) in seconds. These algorithms are able to process large amounts of data and recognize patterns that a human could not. IBM, Microsoft, and many other companies have begun to utilize the collection of medical data (X-rays, medical records, books, previous diagnosis symptoms, patient records, etc.) to help doctors make a more accurate diagnosis.
Hospitals and universities are partnering with these companies by providing them with data and utilizing their machine learning algorithms. The adoption of Data Diagnosis is seeping into the medical industry due to the accuracy of these algorithms. Billy Kim, a medical doctor at the University of North Carolina School of Medicine, conducted a study pertaining to cancer treatments using IBM’s Watson. Kim, when talking about IBM’s Watson, stated, “It can quickly extract key information from a huge amount of scientific data and reveal insights, patterns, and explanations that we might not have discovered on our own.” Another cancer study conducted at the University of North Carolina School of Medicine using IBM’s Watson found that 99% of the time Watson recommended the same cancer treatment as experts. 30% of the time Watson found more treatment possibilities that the experts had not seen.
Assisted medical diagnoses based off machine learning algorithms are becoming more prevalent. Data Diagnosis, what was once fictional, is now more real than ever, and it is only the beginning. Doctors will still be necessary, but how necessary? Although studies are still being conducted, would you trust your doctor’s opinion if he or she referred to an artificial intelligence model for your treatment?