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article imageHealthcare needs AI for improved business intelligence

By Tim Sandle     Apr 8, 2018 in Health
As with other businesses, healthcare has discovered the imperative of linking artificial intelligence capabilities with human activities in order to boost competitiveness.
Artificial intelligence can help pharmaceutical companies to leverage improved data insights in many ways. These include reviewing and interpreting comprehensive datasets; running speedier development cycles; interpreting data in context; and other types of business intelligence. Most significantly, machine intelligence seems to be able to solve the problems that have perpetually caused road blocks with pharmaceutical development, notably time for drug discovery and the subsequent clinical trial success rate.
These ideas are explored by Gunjan Bhardwaj is the founder and CEO of Innoplexus, writing for PharmaPhorum. He makes the point that, in terms of improved scrutiny of datasets, platforms that work more like “a window into the world of available information” as opposed to “high-priced collection of limited data”, are required. To do means intelligent seams that can cross-reference from multiple data sources. An example is linking clinical trial data with government health databases.
Artificial intelligence can also assist with shortening the time to achieve data insights. Here Bhardwaj draws on the example of Tramadol. This was a weak prescription opioid for pain relief. The drug later became one of the abused opioids afflicting part of the U.S.’s problem with prescription painkillers.
Given that warning signs about the medication were available in databases, Bhardwaj notes that artificial intelligence could have analysed this and, from trend analysis, prevented a costly lawsuit for the pharmaceutical companies involved.
Artificial intelligence can also play a part with delving deeply into medical data, such as complex data of vital sign readings to chemical structures. Here artificial intelligence is especially effective at removing the context-specific ‘noise’ from the data sets.  
With these example, Bhardwaj summarizes that artificial intelligence can generate detailed datasets and can perform in-depth data analytics more accurately and across multiple contexts, in better ways than conventional computer systems of people.
The use of artificial intelligence in pharma is supported by Eric Horvitz, director of Microsoft Research Labs in Redmond, Washington. Given how developing new medicines takes about a decade of research and an expenditure of $2.6 billion to foster an experimental drug from design to market, artificial intelligence holds the promise of making the drug discovery process faster and cheaper.
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