Artificial intelligence (AI) in the pharmaceutical industry is growing as “the global artificial intelligence market size was valued at USD 136.55 billion in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030,” according to Grand View Research.  

To understand how this growth deepens, we speak with Director of Product Solutions Zach Weingarden to answer some of the industry’s most anticipated questions.  

How do you see artificial intelligence (AI) and machine learning technologies changing the way that new drugs are developed and reported in the next 5-10 years? 

Answer: Artificial intelligence (AI) is a transformative tool that will impact many industries, and we are only scratching the surface of its capabilities. Today, the hot topic is language models, with ChatGPT, Bing, and other applications making OpenAI accessible to everyone.  

As an extremely text-heavy industry, this form of artificial intelligence (AI) augmentation is particularly exciting, because it can help analyze data and summarize it into a human-readable text form. This could speed the drug development process tremendously.  

But, this isn’t the only form of artificial intelligence (AI).  

Deep learning, for example, is a form of machine learning that can be used to detect signals in data and inform decisions about potential drug candidates. It can also be used to improve the engagement and recruiting procedures for a clinical trial, based on information about participant demographics or the condition under study.  

What challenges have you faced when implementing artificial intelligence (AI) in the pharmaceutical industry, and how have you overcome them? 

Answer: In my role, the focus is clinical trial transparency. Much of what we do involves the analysis, interpretation, summarization, anonymization, and transformation of text and data. Artificial intelligence (AI) is a tool we use to help perform some of these tasks.  

One major hurdle, by nature, is that artificial intelligence (AI) isn’t necessarily 100 percent accurate. This is where training the model comes into play.

The more information that an artificial intelligence (AI) in the pharmaceutical industry is fed that applies to a specific use case, the better it will perform over time.  

It is also easy to become over-reliant on artificial intelligence (AI) in the pharmaceutical industry, so robust quality control and review procedures are extremely important. 

How do you balance the use of artificial intelligence (AI) in the pharmaceutical industry with traditional drug development methods, and where do you see the greatest potential for AI to add value? 

Answer: I think artificial intelligence (AI) in the pharmaceutical industry can be used to augment any existing drug development method.

The newer, innovative forms of clinical trial design, like decentralized or adaptive clinical trials for instance, can take advantage of artificial intelligence (AI) in unique ways, because they have built-in mechanisms for flexibility where AI can aid in critical decision-making.  

This may lead to further innovation in clinical trial design, speeding up the critical development process and reducing wasted time and resources. But, it can also aid in more traditional methods as well.

For instance, artificial intelligence (AI) can be used to identify the ideal target demographic and inform the inclusion/exclusion criteria for a clinical study.  

It is not necessarily replacing existing procedures that are well-established and effective, but artificial intelligence (AI) can play a critical role in enhancing the pharmaceutical industry. 

What ethical considerations are involved in using artificial intelligence (AI) in the pharmaceutical industry to make decisions related to patient care and drug development? 

Answer: Whether we’re talking about individual patient care or drug development decisions, there must be guardrails on what artificial intelligence (AI) can and cannot do. It should be viewed as a tool, or a resource to help with decision-making, but not a crutch. 

Ultimately, decisions must be made by a doctor, scientist, or other appropriate expert based on the individual situation.

When using artificial intelligence (AI) in the pharmaceutical industry to help guide decision-making, it is important to understand its design, capabilities, and limitations. Each must be weighed in consideration. 

How do you see the use of artificial intelligence (AI) in the pharmaceutical industry evolving in the long term, and what impact do you think it will have on the industry as a whole? 

Answer: It has the potential to benefit clinical trial transparency tremendously. This is an area where the industry still struggles, primarily due to the sheer effort involved in anonymizing clinical study documents that can be extremely long and dense.

Artificial intelligence (AI) can bring a lot of efficiencies in this area, as well as analyzing the re-identification risk in more sophisticated ways.  

Artificial intelligence (AI) in the pharmaceutical industry can improve the clinical data sharing process to the point where we unlock a fully collaborative environment where clinical data is shared freely between responsible research entities.

The language model technology that I mentioned earlier could have a role to play in summarizing research results and developing plain language summaries for clinical trial participants and the public.  

It could be used to educate healthcare providers about ongoing clinical trials and opportunities for their patients, with targeted information driven by data and deep learning. All of this can boost public awareness, health literacy, clinical trial transparency, and generally improve the safety and effectiveness of the drug development process. 

How is your organization investing in and building expertise in artificial intelligence (AI) and machine learning to stay competitive in the pharmaceutical industry? 

Answer: As a software vendor we’ve been using artificial intelligence (AI) in the pharmaceutical industry in various capacities for many years. We continue to add new capabilities to our tool belt as advancements are made in new areas.  

It often seems exciting and even limitless, but the challenge is understanding how best to incorporate these tools in ways that are effective and responsible. This requires a deep understanding of the technology and how it can best address the problems of today and tomorrow. And, we’re proud to be leading clinical trial transparency in this area.  

Have more questions? Ask them here and we’ll have Zach reach out to you.