We have seen it in science fiction movies for decades – from last year’s Oscar-contender Ex Machina, to Stanley Kubrick’s 2001: A Space Odyssey, and all the way back to the 1920s, when Metropolis came to theaters; the human race both fears for and obsesses over the looming possibility of robots, or artificial intelligence (AI), entering our lives and – somehow – staging a takeover.
But as we’ve come to know, especially in the past decade, life imitates art – and the prospect of AI being involved in our lives has shifted from being a mere fantasy to a very real reality. What could be better evidence of this than IBM’s computer, Watson, who made its debut on Jeopardy! and beat gameshow super-champions Ken Jennings and Brad Rutter in 2011? Counter to the Hollywood fables – at least for now – in reality, AI is helping humans in medical fields, such as radiology and oncology.
One company in particular, Enlitic, has garnered popular notice for its role in the future of oncology and its technology aimed at helping radiologists make faster and more accurate diagnoses.
Radiology, Enlitic, and How They Work Together
According to the National Cancer Institute (NCI) at the National Institutes of Health (NIH), cancer treatment begins with identifying symptoms, taking tests to make a diagnosis, staging the extent of cancer, and then suggesting treatment options and discussing the prognosis. One of the vital roles in this process begins with the radiologist – the physician who specializes in the diagnosis and treatment of disease by using medical imaging technologies, such as an MRI or a CT scan.
Though radiologists have specialized training in interpreting medical images, their readings are subjectively based off of what can be seen by the human eye. Because cancer is often difficult to detect due to its subtleties, an AI system like Enlitic can help radiologists make more accurate diagnoses.
Founded in 2014 as a software startup in San Francisco, Enlitic defines themselves as “the deep learning healthcare company ushering in a new era of Data Driven Medicine.” What this means is that Enlitic’s primary focus is in advancing the use of AI to improve medicine. Enlitic has teamed up with Capitol Health Limited, the fastest-growing radiology service provider in Australia. In doing so, Enlitic is able to use valuable tools and data – from scans and images to reports – to feed the “growing brain” of its AI system.
What Enlitic’s founder Jeremy Howard brings to cancer research is his unmatched experience in machine learning, or, more plainly, the ability computers have to receive, interpret, and process data. Similarly, Enlitic enlists deep learning, a process whereby a computer mimics the way that the human brain works by taking large amounts of data (in this case, MRIs, CT scans, ultrasounds, x-rays, and other raw data), using algorithms to look for complex relationships and patterns, and then refining its algorithms as more data is processed.
In doing this, Enlitic arms radiologists with the ability to read scans in a more effective and efficient way. According to Howard, “[Enlitic has] algorithms right now that can find earlier and more accurately whether or not you have lung cancer. If we can find out that early, you have a 400% better chance of survival.”
Cancers that occur in the lungs are some of the most elusive and difficult cancers to detect. Should Enlitic’s initiatives gain traction, it would be promising news for all patients who suffer from difficult-to-detect cancers – like lung cancer or mesothelioma.
Enlitic, Watson, & the Implications of AI in Oncology
Enlitic is not the first biotech company to use AI in an effort to make medical advances. However, it is one of the first to create a vast network of information using data from radiology, pathology, and existing patient medical records. Though Enlitic may be the first to use AI in such an extensive way, it will definitely not be the last. In the all-out war among biotech companies, researcher teams, and pharmaceutical corporations to find a “cure” for cancer, many other businesses are also looking towards AI’s role in healthcare.
In 2012, IBM paired its Watson supercomputer with Memorial Sloan Kettering Cancer Center (MSKCC) to personalize patient care by giving Watson all of the information about oncology based on case histories at MSKCC. Watson was trained to compare a patient’s medical information against treatment guidelines, patient records, published research, and genetic data in order to choose the best diagnosis and treatment options. Last year, IBM announced that it would buy Merge Healthcare, a company specializing in medical images, in an effort to add to Watson’s data sources.
Likewise, at the beginning of this year, Bill Gates and Jeff Bezos led funding for Grail, a company developing a simple blood test that would be able to detect any kind of cancer. This technology, in the perfect scenario, would create a non-invasive way to detect cancer before symptoms appear.
With the push of interest and investment in AI agents, from Enlitic to Watson, in medicine, particularly in radiology and cancer treatment, the chance of increasing cancer detection and therefore cancer survival seems likely. With AI agents that are able detect cancer faster and more accurately, patients will receive better treatments and have better chances of living. It seems that rather than staging a hostile takeover, artificial intelligence may instead be the future in helping us survive.