By Jerry Chapman, GMP Consultant and Editor-in-chief, Xavier Health
Experts hope to learn how to augment human decisions with AI so decisions are more informed. U.S. Food and Drug Administration (FDA) officials and leaders in the pharma and medical device spaces agree artificial intelligence (AI) tools could enable a step change in quality management in those industries. Areas that could be impacted include supply chain management, lot release, manufacturing, compliance operations, clinical trial end points and drug discovery, among others.
AI has drawn the attention of the pharma industry recently based on impressive successes in other industries, such as machines performing face recognition, driving vehicles, competing at master levels in chess and composing music. To date, the primary applications of AI in pharma have been in R&D and clinical applications. These include predicting Alzheimer’s disease, diagnosing breast cancer and precision and predictive medicine applications.
The Xavier Health Artificial Intelligence Initiative brought together key players and experts from industry, academia and government in August 2017 to explore the possibilities and potential roadblocks. At the FDA/Xavier PharmaLink conference in March 2018 at Xavier University in Cincinnati, Ohio, two working groups that are part of the initiative gave preliminary readouts of their progress to date. More in-depth summaries will be provided at the Xavier AI Summit in August 2018, along with more detailed discussions of the use of AI in pharma and medical device companies.
The Xavier Health AI Initiative is working to expand the use of AI across the pharma and device industries. Its task is to identify ways to implement AI across quality operations, regulatory affairs, supply chain operations and manufacturing operations — augmenting human decisions with AI so decisions are more informed. The vision is to use AI to move the industry from being reactive to proactive, to predictive and eventually to prescriptive, so that actions are right-first-time.
The intent is to increase patient safety by ensuring the consistency of product quality. The initiative aims to promote a move from traditional pharma techniques — such as plant audits and product sampling, which are snapshots in time — to continuous monitoring of huge amounts of GMP and non-GMP data to produce continuous product quality assurance.
What Is AI?
Simply put, AI is shorthand for any task a computer can perform in a way that is equal to or surpasses human capability. It makes use of varied methods such as knowledge bases, expert systems, and machine learning. Using computer algorithms, AI can sift through large amounts of raw data looking for patterns and connections much more efficiently and quickly than a human could.
An AI variant, deep learning, breaks the solution to a complex problem into multiple stages or layers. It examines data sets and discovers the underlying structure, with deeper layers refining the output from the previous ones. A mature system has fully connected layers with both forward and backward comparison abilities.
Another AI subset known as machine learning relies on neural networks — computer systems modeled after the human brain. It involves multilevel probabilistic analysis, allowing computers to simulate and perhaps expand on how the human brain processes information.
Are There Any Red Flags?
As these machines “learn,” the pathways they take to arrive at decisions change, so the original programmers of the algorithms cannot tell how the decisions were arrived at. This creates a “black box” that can be problematic for highly regulated industries, such as pharma, where the reasons for decisions and actions need to be documented.
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