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Artificial Intelligence (AI) is a growing technology that finds its application in almost every aspect of life and industry. Likewise, the pharmaceutical industry is finding innovative ways to use powerful technology to resolve the challenges facing by pharma today. The increase in size and a greater variety of biomedical datasets are the factors that contributed to the growth of AI in the pharmaceutical industry. Exploration of AI in pharma will fall into three major divisions: discovery, development, and commercialization. When using AI within the pharmaceutical process, it is critical to remember AI is best suited for repetitive tasks where efficiency lacks. AI-driven pharmaceutical duties must be low risk, harmless, and reduce costs.
Pharmaceutical companies are incorporating AI most in drug discovery because this segment does not impact patients and therefore have a low risk from that perspective. Another reason might be that supercomputing has strong roots in drug discovery. Machine learning technology includes a variety of parameters such as genomics, molecular and cellular structure databases have been established. There is a volume of data incorporated in these systems humanly incomprehensible on which AI can carry out an efficient analysis. AI is more efficient in predicting potential prospects in drug discovery and is expected to surpass human capability and aid with the identification of more therapies to treat more diseases in a shorter timeframe.
The drug development process requires much more of human wisdom, experience, intuition and the ability to pivot quickly. Calling help of AI to both identify patients for clinical trials is becoming a standard. Getting the right patient to enroll in the clinical trial over competitors is crucial. AI companies in healthcare are now offering patient screening and engagement in addition to patient identification. Even some pharma companies are beginning to develop their own clinical trial AI.
The large volume of data needed during marketing is unstructured which complicates the evaluation from an AI perspective. Healthcare at heart is about better patient care, so the efforts that directly impact patients matter. Personalization of medicine is an AI development effort within commercialization. The broader magnitudes of AI development efforts include precision medicine/predictive medicine, medical diagnosis and radiology applications, customer service bots, and cross-company quality assurance.
The future success of pharmaceutical companies will belong to the leaders who properly incorporate AI in their product work streams.
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