In the very near future, you may visit your doctor and receive a drug therapy that was customized just for you based on your DNA, metabolism, and lifestyle. Advances in key technologies, including decoding the human genome (DNA), artificial intelligence (AI), and health information, are the catalysts for the rapidly accelerating field of personalized medicine. Navid Alipour, JD (Moderator) Co-founder and Managing Partner, Analytics Ventures Navid Alipour is Co-founder and Managing Partner at Analytics Ventures, a Venture Formation Fund focused on starting new ventures with artificial intelligence and machine learning at their core. Prior to co-founding Analytics Ventures, he founded La Costa Investment Group, making investments in startups nationally. Through the founding of multiple Artificial Intelligence(AI) based companies like CureMetrix and CureMatch, Navid is a long-time entrepreneur in the AI space, and looks to address the need between angel investors and big venture capital funds.

The invasive and expensive diagnosis process of bladder cancer, which is one of the most common and aggressive cancers in the United States, may be soon helped by a novel non-invasive diagnostic method thanks to advances in machine learning research at the San Diego Supercomputer Center (SDSC), Moores Cancer Center, and CureMatch Incorporated. Research scientists Igor Tsigelny and Valentina Kouznetsova have been working on the development of a machine-learning (ML) model that looks at a patient's metabolites and their chemical descriptors. The model accurately classifies the stages of bladder cancer in a patient, according to the researchers. Tsigelny is the lead author on a recently published study in the Metabolomics journal called'Recognition of Early and Late Stages of Bladder Cancer using Metabolites and Machine Learning'. When a patient experiences early symptoms of bladder cancer (e.g., blood in urine, pain during urination, etc.), the current method of diagnosis is often a painful, invasive series of tests.

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.

This story is part of an ongoing Xconomy series on A.I. in healthcare. 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?