Abstract

Big data in the life sciences and healthcare sectors is an increasing trend. It is the processing and displaying of huge volumes and varieties of data at a rapid speed. There is a vast amount of data in the healthcare and Pharmaceutical industries, including lab data, insurance data, patient records, research data, and even social media data (1, 2). Pharmaceutical companies have vast amounts of compounds that could be the perfect solution to combat specific diseases, but they have no way to identify them as such. The development and production of drugs can cost pharmaceutical companies up to $2.6 billion (£1.8bn) and take 12 to 14 years to complete (1). Artificial Intelligence (AI) plays a crucial role in enabling the industry to achieve these objectives, be it analytics in personalized medicine, cloud computing in collaboration, or wearable devices in remote and self-health monitoring. Thus, the main short/medium-term implication AI has for the pharmaceutical industry is the reduced time it takes to develop drugs and thus the associated costs, enhancing return on investment and could even mean a reduction in cost for end users. As the pharmaceutical industry becomes increasingly more connected, information and communication technologies will fundamentally reshape both the consumption and delivery of medications (1, 2,3)

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