Optimising artificial intelligence in the pharmaceutical industry
Artificial intelligence (AI) has wide-reaching potential within the pharmaceutical industry, from clinical trials to marketing and sales analytics. Using a machine learning programme can reduce the time spent on examining data, saving money and allowing researchers to focus on other issues.
European Pharmaceutical Review’s Victoria Rees spoke with Christopher Rafter, Chief Operating Officer (COO) of Inzata Analytics, to discover more about AI in pharma and its capabilities.
As a machine learning system, AI allows researchers to collect and analyse data effectively, Rafter explained. He described the basic principle; the more data it analyses, the more it will improve. This means that over time, models will continue to collect better due to an accumulation of information.
However, this creates a significant challenge for data scientists when working with small amounts of information. As these researchers usually have a limited amount of time, they cannot take advantage of an optimum amount of data to fully develop their models.
Therefore, using third-party data provides a solution, allowing scientists to pool information for their AI systems.
One use of AI that Rafter discussed is its ability to predict which R&D projects will prove successful and move on to clinical trials. He said that this is something pharma companies are “very interested in,” as it can save time and money during the development stage. However, this is not the only advantage that AI can lend to pharmaceutical companies.
Rafter highlighted that the main areas of AI usage in the pharmaceutical industry are R&D and marketing. He said that as pharma is a sales-driven industry, AI can be a useful tool to refine marketing decision-making and strategies.
The amalgamation of print, digital, direct and other marketing activities can have a wide impact on the sales of a drug. Knowing which methods are most successful is useful for companies to ensure they explore the most profitable avenues. Using AI to chart a customer journey can allow a business to identify the direct marketing messages that they have been exposed to and which led to a purchase.
Rafter commented that as sales and marketing have become “more scientific,” having AI to analyse data from past campaigns allows companies to invest in the most lucrative schemes. He also commented that this is most noticeable in the US, where pharma businesses are more focused on sales compared with the EU or other parts of the world. Therefore, analysing which drugs patients are buying is an integral part of the US industry that AI can help improve.
One of the challenges of using AI in the pharmaceutical industry is the availability of resources and access, said Rafter. A potential solution to this is to simplify AI models so that users can input data without complication.
Another issue he highlighted is the social and institutional understanding of AI.
