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4 Issues Holding AI Back In Life Sciences

January 10th, 2019

Artificial intelligence

There's hardly an industry that artificial intelligence hasn't touched. In one form or another -- machine learning, chatbots and predictive analytics, for example -- AI is rapidly becoming a part of all technologies. The life sciences are no exception, though many professionals believe the industry is lagging in its adoption of AI.

Last summer's survey by The Pistoia Alliance found 42% of life sciences professionals doubting that AI has yet proven its value. According to the results 21% "felt that their projects were not yet providing meaningful outcomes, and 21% ‘didn’t know’ if projects were delivering meaningful outcomes."

“This survey shows interest in AI remains strong, but there is still a challenge with moving past the hype to a realty where AI is delivering insights with the power to truly augment researchers’ work,” Dr. Steve Arlington, president of the Alliance said at the time the survey was released.

Just recently, he authored an article for listing four barriers he said the industry must overcome before AI will show the dividends its proponents predict:

  1. Skills shortage -- Arlington calls this one of the biggest challenges the industry faces in making AI a productive tool. Part of the blame is the pay discrepancy between tech and life science; part is also the reputation pharmaceutical companies are getting for “hire and fire." The way out, he says, to upskill existing staff while countering negative impressions.
  2. Poor data -- "Limited access to quality data is also affecting the results AI can currently yield."
  3. Lack of data standards -- The industry has no standard for collecting patient data, which means "significant time and resources are required to integrate data into corporate systems and make it usable."
  4. Anxiety -- Progress has been "hindered by anxiety over change, such as the ethics of AI, and employee concerns over potential job losses."

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