The Role of the Quality Professional in the Evolution of AI in BioPharma and Medical Devices

In today’s world, artificial intelligence (AI) and its rapid, widespread development is gaining tremendous interest.  As AI technologies continue to evolve, individuals and businesses around the globe, large and small, are utilizing AI to assist in their own innovations and growth. Industries, including biopharma and medical devices, are using AI for product research, development, and manufacturing. AI has become one of the most pervasive elements of our time, one that represents one of the fastest-growing uses of technology in recent history. As with all new technologies continually evolving, we imagine the promise of AI now and into the future. However, we do not yet have all of the necessary technical knowledge to fully understand its capabilities.  Are we as an industry close to actually achieving its promise?  Today’s aspirations are great, but can implementation occur tomorrow, or is it years away?

In this article, we will consider the promise and possible quality related applications of AI, the current state of the art today, along with some insights on factors that are critical to successful realization of the promise, and the implications for Quality professionals.

INSIGHT 1:  Manage our fear by being informed

All of us that have the privilege of working in an industry that protects and saves the lives of those threatened by illness and disease understand the responsibility we have to ensure that we get things right. In the highly regulated biopharm/biotech industry, quality, purity and safety is critical to developing and manufacturing a product; without the quality function, compliance with regulatory requirements is threatened. 

We have seen many new technologies emerge that showed great theoretical promise but, in their infancy, were mystifying. With the unknown comes fear and uncertainty. Before we could move these technologies into practice, we needed to be fully informed about the technology itself, how it works, potential risks, how the risk could be mitigated, and ultimately why the application would in no way jeopardize the quality, purity, safety, and efficacy of our therapies. This is as true for AI as any case we have encountered. This means Quality professionals must possess and demonstrate a functional understanding of the technology. Organizations should be looking ahead to determine how this will affect the role of the Quality professional.  With AI being used as a valuable tool, Quality professionals will utilize it to accomplish their objectives, speed up the processes, and improve the quality of the projects.  Therefore, these professionals should honestly ascertain their skills and consider if they are sufficiently knowledgeable and therefore compatible with AI. 

This is necessary for them to understand the ways product quality and safety can be reliably adapted for use and integrated into quality management systems.  The Quality professional will also use this knowledge to work with other functions within the industry to support valid, reliable, and safe applications of AI across the product lifecycle. Finally, the Quality professional can play a highly valuable role in sharing that knowledge with regulatory agency representatives to ensure their level of confidence in AI. Regulators are responsible for protecting the public and Quality professionals can provide the knowledge and data regulators need to embrace AI applications.  

INSIGHT 2: Integrating the technological capabilities and the work in our industry

Clearly, AI has the potential to do amazing things.  Everyone is aware of AI, but how many of us truly understand it well enough to properly integrate it into our regulated environment?  Doing some research, I found that while there are exciting aspirations for use of AI to improve and accelerate our industry processes, the integration of AI into actual applications is at a very early stage.  For example, many envision AI as an accelerated way to write standard documents such as clinical and product development plans, investigations, validation plans and the like. This very valuable goal should be vigorously pursued. With this in mind, companies would be wise to begin NOW to thoroughly evaluate use cases with the highest probability of success. This includes risks connected to limitations in the AI technology as it exists today, the feasibility of applying it to industry operations, and time, cost and training associated with implementation.  This will enable rapid and effective implementation as the technology advances.

INSIGHT 3:  It is all about the reliability of the data

Consider this – a basic premise of AI applications is that there must be a valid source of the knowledge that the AI system learns from.  We all know the classic saying regarding data collection – garbage in, garbage out.  Clearly, the output AI provides must receive initial input – a prompt, if you will.    Given that people are adapting to the evolution of AI it is completely reasonable to  anticipate that, in the foreseeable future, AI platforms could be used to generate base documents, procedures, investigational reports, project plans, clinical plans, validation programs … the list goes on.  AI could conceivably create a very good starting point, but in each of these applications, unique data will be required that AI would be unable to initiate on its own.  

This rapid advancement of the technology is shining a bright light on how incredibly important the validity of source data is – the data that feeds machine learning (ML). Because if, in fact, it is fed incomplete, inaccurate, unreliable data, the output will be incomplete, inaccurate, and unreliable, as well.  If I was a quality leader, with aspirations to leverage advanced technologies like AI, I would be thinking about where my organization is today – its data integrity, the quality of source data that currently exists, and what work might need to be done to improve the quality of the existing data.  Do I plan to use it, or should I consider making changes now to the way I manage data?  So that from this day forward, I can count on what I have and what I build upon so that when the AI technology is fully useable, I know I can rely on what I have to ensure a good output.  

INSIGHT 4: Quality professionals need new skills

Systems that use advanced technologies to obtain the maximum benefit are exceptionally cutting-edge; likely, many Quality professionals may not yet have technical skills needed to operate AI systems or evaluate the compliance of such systems. Start thinking about where your organizational and technical skills sets are today versus those in the future, and start developing employees’ capabilities so that when the time comes, your organization and staff will be ready.  It is important to start this now, because AI’s evolution into a daily tool used in a wide-ranging array of endeavors is not far off.  Do what you can now to get employees’ skills and knowledge ready, to ensure they understand how to use AI as part of the organization’s workflows.  Identify how your company intends to use AI in routine workflows, particularly in quality. What will the job description for Quality professionals look like? How will the quality function and the role of the Quality professional change?  Acknowledge that differences will transpire, and start to engage employee participation in this planning as a part of their development.

INSIGHT 5:  Prepare now for the new world

The introduction of AI into the biopharm/biotech world will be a very large change.  Successful use of the technology will depend heavily on a well-developed change management program. Management must understand how the human element will be affected going forward.  Employees understandably will question how changes will relate to them, including whether their positions will be safe when these changes occur. Have open conversations with employees regarding AI and the potential effects it will have on the organization. Ensure staff members are aware of, understand upcoming transformations, and are not anxious or afraid of what these changes could potentially have on their jobs.  Make sure they understand that even if the AI programs work, people will still be required to implement these programs, manage them, provide effectiveness checks, and demonstrate the viability of the systems. Certainly, all of this will begin with a validation process, which will demonstrate if the systems are actually doing what they are supposed to do.

In summary, AI applications do not yet fully exist in the sense that most people think; AI capabilities are not yet altogether operational. The day is likely coming sooner rather than later when they will be available. As these capabilities continue to evolve at a dramatic, ever-increasing rate, it is critical for organizations to prepare to ensure they are ready as AI continues to expand over time, and for Quality professionals to adapt to their new normal as their roles evolve along with AI. 

About Bob Castellucci

Bob Castellucci is the Principal Consultant and Networking Executive at PQE Group. He is a seasoned professional with over four decades of experience in the pharmaceutical and medical device industries. As Principal Consultant and Networking Executive at PQE Group US, Bob is leveraging his extensive background in quality, compliance, and remediation to help drive the company's growth in the U.S. market.

Bob holds both Bachelors and Master’s Degrees in Biology from Villanova University. His career began at Merck, where he developed a deep understanding of resourcefulness and problem-solving. Over the years, he has built quality teams and systems in various companies, including serving as the head of quality at Vertex Pharmaceuticals. Bob also founded Partnership to Prosperity, a program designed to ensure job seekers develop the skills employers need.