AI and machine learning have impacted our daily lives and the way business decisions are made dramatically the past few years. AI’s rise to fame is most apparent in consumer goods, a prime example here is the use of natural language processing in personal assistants like Siri and Alexa, changing the way we interact with our devices. But also in healthcare AI has made a difference, for instance by vastly improving the efficiency of CT scan and human genome analysis.
One common denominator is that AI helps making sense of a massive and continuous stream of information. This can improve the decision-making process and is widely applicable, also in the pharma industry.
If there’s one thing the healthcare and pharma industry has in abundance it is data. AI offers the potential to tap into that data at an unprecedented rate, unveiling information and patterns hidden within. Moreover, when the AI is given more data to analyze, it becomes smarter. This intelligence can in turn be used to help make informed decisions.
In the pharma industry, AI is most used in Drug Discovery and Manufacturing, Clinical Trial Research, Manufacturing Improvements, Risk monitoring and Marketing. A more concrete use case is regulatory decision making, where Machine learning has already been utilized to determine the safety of a drug, detecting anomalies that might otherwise slow the progress of a drug’s development.
So if the benefits seem straightforward, why haven’t we all adopted yet? One of the challenges of using AI in the pharmaceutical industry is not only the availability of resources and access to right tools, but the complexity of the AI models, limiting user friendliness.
Another issue is the social and institutional understanding of AI. Due to the various ways in which AI can be modelled, it can be hard to explain how it works, making adoption even harder. Although a model can be mathematically proven, its reasonings can be difficult to articulate, shrouding AI in mystery.Looking ahead, a big unknown is if healthcare and pharma will be able to incorporate these technologies in their daily operations as they become available. For instance: Will the regulatory environment be able to change as quickly as AI is able to provide rapid results?<:em>