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Adopted from a discussion with our Computer Systems Quality team at Zigzag Associates
As clinical research integrates new technology to innovate at pace, how will we as clinical quality professionals, ensure this technology is adopted safely and to global guidelines?
New tech is coming. Whether its Robotic Process Automation, Computer Vision, Machine Learning, or other advances which fall into the Artificial Intelligence umbrella, regulatory bodies and quality professionals will need to adapt. Clinical trials will need to remain safe and effective, while technology enables the pace of innovation to accelerate.
But how do you keep both pace and data governance and integrity to global regulatory standards?
In both the near and mid-term, A.I. we be piloted then adopted to accelerate the capacity of Pharma and Biotechs to innovate, delivering life changing molecules to patients in need. With recent advances being made, organizations are beginning the process of piloting these technologies. Within a year, this will progress, and certainly enormous strides forward will happen within 5 years. What inspiring results we will witness because of the adoption of A.I. is naturally speculative at this stage, but it will more than certainly make an enormous impact in the industry’s ability to innovate at pace.
Our Computer Systems Quality Assurance (CSQA) team is working with several organizations who are piloting new A.I. and other Tech into their clinical workflows. Between the need for monitoring devices supporting decentralized clinical trials, more real time monitoring of data with cloud storage warehouses, and additional early stage adoption technologies, regulatory guidelines must lead the way in how to ensure data governance and integrity is paramount. Of course, each individual organization will have unique technological needs and challenges stemming from those needs. Organizations need support in tailoring their processes to their individual need, priority and of course tie into regulatory guidelines to protect their life changing molecules through the approval process.
How do we prepare Quality Professionals for New Technological Advancements?
One obvious challenge for Quality Professionals is auditing and validating Artificial Intelligence Technologies but also and even more difficult, the complexity of clinical trials where technology is also being piloted and implemented. Varied techniques and methods that can be implemented currently include:
Complex mathematical
Psychological
Ethical and training standards
The bigger problem in developing a system to keep pace with validation of new technology will be development of novel auditing and training standards. This will ensure there is full oversight over Artificial Intelligence Technological capabilities, and forefront continued development to fully understand the limitations, of which there most certainly will be some recognized in the coming years.
There is precedence in the development of objective knowledge about the capability and limitations surrounding the adoption of tech in high stakes industries. NASA has written articles about this and is probably the institute with the most objective knowledge about A.I. and additionally, which this blog is not covering, Quantum computing limitations. Luckily, there are global Think Tanks pondering these deeply divisive questions. Most companies working with Quantum computing and advanced A.I. have ethical departments in place, potentially opening up a new profession, or certainly, area which our industry needs significant training and development within the compliance and Quality realm of Biotechs and Pharma.
In Closing:
Irrefutably, the integration of A.I. will enhance the ability of the industry to innovate at pace. The solution(s) to quality compliance are several fold and non-straightforward. These solutions will need to evolve as research and development quality professionals how A.I. will work within the discovery to market process.
There is an imminent, global need for regulations and ethical guidelines, laws and rules surrounding the development and use of A.I. within clinical research and development of novel medicines.
Lessons may be transferred from other regulated industries which have successfully adopted A.I..
Adoption of procedures and new methods will need to proceed the adoption and integration of A.I. into development workflows.
Comprehensive training & education on new processes and procedures will need to go hand in hand with the procedures being adjusted to new workflows.