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Mareana Included in Gartner Research: Cited Among “Clinical Vendors Building Value with AI Technology”

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Mareana announced that it has been included in Gartner’s January 17, 2019 artificial intelligence (AI) research note, Life Sciences CIOs, Accelerate Clinical Development with New Applications of Artificial Intelligence, written by analysts Jeff Smith and Michael Shanler.

“We are honored to be included in this Gartner report,” says Christopher Knerr, Mareana Co-Founder and CEO. “We believe our inclusion in the research note validates the value-add of qSuite™, our business orchestration platform that uses AI and advanced analytics to help companies create value from their unstructured data.”

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According to the report, Gartner notes that enterprise professionals engaged in active AI initiatives cite “four major hurdles hold back the value of AI: 79% report ‘fear of the unknown’, 63% report an ‘inability to find a starting point’, 48% report ‘incorrect vendor strategy’, and 40% report ‘low enterprise maturity’.”1 Yet there are numerous benefits to developing and deploying AI capabilities including reduced clinical development costs and increased speed to market.

Education is the first step in developing a sound AI strategy. And much insight can be gained by researching AI use cases across industries. According to Gartner, “to categorize their readiness to deliver business value, Gartner divides industry use cases into four solution categories: conceptual solutions, emerging solutions, evolving solutions, and demonstrating solutions.”1 The research note says, “due to the wide array of AI technologies available, choosing the correct match for a business process — like safety signal detection or clinical trial risk optimization — is a major challenge.”

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Beyond education, companies must match their AI strategies with their capabilities and access to subject matter experts. According to Gartner, “a common challenge CIOs face is that many users on the business side are not familiar with the varying technical requirements between AI platforms and applications. This makes moving from a proof of concept (POC) into production a challenge.”1 The research note further states, “Scaling from a POC to a large-scale rollout is commonly made difficult or impossible due to factors such as lack of appropriate IT resources and expertise, data quality concerns, and infrastructure and platform requirements.”

Mr. Knerr adds, “AI plays an increasingly vital role in clinical development. Today’s CIOs need well-vetted tools, and the support of external vendors, to move from problem identification through POC to full-scale implementation.”

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