This story is part of an ongoing Xconomy series on A.I. in healthcare. Other stories cover big-company efforts, a genomics hackathon, and the impact on doctors and patients.
These are heady times for using artificial intelligence to extract insights from healthcare data—in particular, from the tidal wave of information coming out of fields like genomics and medical imaging.
Yet as innovations proliferate, some age-old business questions have come to the fore. How can startups make money in this emerging field? How can healthcare companies use AI to “bend the curve” of increasing healthcare costs? And, ultimately, how can they get buy-in from government regulators, insurers, doctors, and patients? These were some of the issues that emerged this spring when Xconomy brought together some of San Diego’s most-prominent tech and life sciences leaders for a dinner discussion about the risks and opportunities in the convergence of AI and healthcare.
“Being a healthcare investor, I love the fact that there’s interest now on the tech side,” said Kim Kamdar, a partner in the San Diego office of the venture firm Domain Associates. “It opens up a whole new avenue of potential co-investors for our companies.”
The consensus: It’s still early days for applying machine learning and related techniques in healthcare, and it’s hard to foresee how these innovations will play out. As Xconomy senior editor Jeff Engel has reported, questions abound over the impact AI will have on doctors and healthcare institutions. Yet there is little doubt that transformational change is coming, and tech companies ranging in size from small startups to corporate titans like IBM and GE are scrambling to gain a foothold in this emerging field.
If ever there was a sector in need of transformational disruption, it would be healthcare, where spending in the United States amounts to more than $3.2 trillion a year—and accounts for close to 18 percent of the U.S. gross domestic product.
The sector represents a lucrative-but-daunting target for investors—complicated by regulatory issues, a healthcare system that separates the interests of patients, providers, and payers, and an investment timeline that can take 10 years or more to realize.
There may be no better example of the potential opportunities than Grail, the $1 billion-plus startup spun out by Illumina (NASDAQ: [[ticker:ILMN]]) to advance diagnostic technology sensitive enough to detect fragments of cancer DNA in a routine blood sample. Yet cautionary tales also abound—most notably with Theranos, the venture-backed diagnostic company that was valued at $9 billion in 2015—and plunged last year to less than a tenth of that.
Interest in healthcare AI runs high in San Diego, which has a well-established life sciences cluster and is home to two genome sequencing giants: Illumina and the life sciences solutions group of Thermo Fisher Scientific (NYSE: [[ticker:TMO]]). San Diego also has some resident expertise in neural networking technologies that accompanied the rise of HNC Software, a developer of analytic software for the financial industry that is now used by FICO (NYSE: [[ticker:FICO]]) to predict credit card fraud, among other things. (FICO acquired HNC in 2002 in a stock deal valued at $810 million.)
The dinner conversation that Xconomy convened included Kamdar and other local investors, data scientists, healthcare CTOs, startup founders, academic researchers, and digital health executives. The kickoff question: Is there a proven business model for startups that are applying innovations in machine learning in the life sciences?
The model that came to mind for Larry Smarr,
