Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".
Artificial intelligence in Pharma refers to the use of automated algorithms to perform tasks which traditionally rely on human intelligence. Over the last five years, the use of artificial intelligence in the pharma and biotech industry has redefined how scientists develop new drugs, tackle disease, and more.
Artificial intelligence can be applied to almost every part of the pharma industry. Artificial intelligence (AI) has wide-reaching potential within the pharmaceutical industry, from clinical trials to marketing and sales analytics. Using a machine learning program can reduce the time spent on examining data, saving money and allowing researchers to focus on other issues.
As a machine learning system, AI allows researchers to collect and analyze data effectively. The basic principle is that 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.
AI areas in Pharma
The main areas of AI usage in the pharmaceutical industry are R&D and marketing”
Therefore, using third-party data provides a solution, allowing scientists to pool information for their AI systems.
One use of AI is its ability to predict which R&D projects will prove successful and move on to clinical trials. 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.
As pharma is a sales-driven industry, AI can be a useful tool to refine marketing decision-making and strategies.
The mixture 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.
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, analyzing 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. A potential solution to this is to simplify AI models so that users can input data without complication.
Another concern 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 logic can be difficult to articulate.
A growing trend is companies requesting the documentation and explanation for the decisions that AI makes, especially when they can affect an individual or private citizen. Very few states in the US have laws that enforce this, and that the EU is also considering similar legislation. Depite this is not a major concern, it is growing, which could affect companies if regulations become stricter, requiring the logic behind AI to be better explained.
However, when the benefits outweigh that costs, companies will be willing to overcome some complications and increase the usage of AI.
A by-product of collecting such large amounts of information is that it needs to be protected. This can be achieved through encryption, secure encryption and transport and encryption at rest.
Many companies are beginning to think of data like oil; as a raw material, the faster it is refined, the better quality it retains. Therefore, the faster that information can be processed and stored securely, the less vulnerable it is to cyber-attacks.
Hackers want to obtain raw data, as this allows them to identify individuals and their private information. As such, companies must protect this as soon as possible. Businesses seek trend and aggregation data summaries from their information, so the faster they can transfer data from a raw state, the faster it will be protected.
AI usage will grow, and although it could potentially see a slow uptake, the number of decisions delegated to machine learning will increase over time.
Its growth will increase and as long as companies can overcome some of the restrictions or roadblocks mentioned earlier, this will continue because there is “merit and value” to what it can provide to those companies.
Healthcare is still “under-penetrated” by AI compared with other industries, mainly due to the conservative nature of the industry; there is a bias to rely on processes that have been successful in the past.
Overall smaller companies are more willing to take risks and invest into AI systems. Smaller or third-party business are more agile, whereas larger pharmaceutical companies are less willing to embrace change. So, although the growth of AI may be slow, it will continue steadily.
A study published by the Massachusetts Institute of Technology (MIT) has found that only 13.8% of drugs successfully pass clinical trials. Furthermore, a company can expect to pay between $161 million to $2 billion for any drug to complete the entire clinical trials process and get FDA approval.
With this in mind, pharma businesses are using AI to increase the success rates of new drugs while decreasing operational costs at the same time.
By Aliyev F.