proven out a business model…[It’s still] early days.”
Navid Alipour, a co-founder and managing partner of San Diego’s Analytics Ventures, said his firm’s portfolio company CureMatch is taking a direct-to-consumer approach, in which cancer patients pay CureMatch to recommend the top three combinations of chemotherapy drugs for each patient’s cancer. The recommendations, based on information in a patient’s own medical record, is intended to help cancer specialists choose a treatment regimen. CureMatch says it uses supercomputer processing to sort through millions of possible three-drug combinations, assessing each combination for factors like unwanted drug-drug interactions, and correlating genomic data to rank the best drug combinations for a specific patient.
CureMetrix, another company in Analytics Ventures’ portfolio, uses machine learning to analyze mammography images for breast cancer—and must still get FDA approval before it can be used in the United States, Alipour said. “It will be a [software as a service] model,” Alipour said. “But we have an institutional investor in Mexico that’s taking us into the top levels of the government there. Breast cancer is a huge problem in Mexico, and there are not many radiologists with a mammography expertise in the country. We’re licensing to the entire country because they have a national healthcare system. So that’s something to think about if you’re outside the U.S. and our insurance system.”
CureMetrix is one of many companies, big and small, that have been applying machine learning to identify anomalies in diagnostic imaging, and image-based pattern recognition seems like “the ultimate use” of the technology, Jimenez said. “But all you have to do is go to [the Strata Data Conference], which is kind of ‘the event’ for big data and data science for the tech community, and the keynote speakers talk about how difficult that use case really is. So you know, it’s maybe not for 10 years…maybe a little bit longer.”
So, when might AI systems supplant radiologists?
Smarr said he was doubtful that artificial intelligence would replace radiologists altogether. Rather, he believes the technology will be used to augment human capabilities—making the worst radiologist more accurate than the best human radiologists could be on their own. “So what you are doing is bringing up the human talent level by augmenting it with vast amounts of data they could never have experienced themselves,” Smarr said. “And I really think that could be more productive in the short term, meaning in the next several decades.”
For companies like DexCom that are focused on the diabetes epidemic, Jimenez said the holy grail is modifying patients’ behavior. That would mean combining the stream of data from glucose monitoring, insulin measurements, patient activity and meals, and applying machine learning to derive insights so the software can send alerts and recommendations back to patients and their doctors, she said.
“But where we are in our maturity as an industry is just publishing numbers,” Jimenez explained. “So we’re just telling people what their glucose number is, which is critical for a type 1 diabetic. But a type 2 diabetic needs to engage with an app, and be compelled to interact with the insights. It’s really all about the development of the app.”
The ultimate goal, perhaps, would be to develop a user interface that uses the insights gained from machine learning to actually prompt diabetic patients to change their behavior.
This point was echoed by Jean Balgrosky, an investor who spent 20 years as the CIO of large, complex healthcare organizations such as San Diego’s Scripps Health. “At the end of the day,” she said, “all this machine learning has to be absorbed and consumed by humans—to take care of humans in healthcare.”
