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HOW THE USE OF AI IS IMPACTING LIFE SCIENCES MANUFACTURING
By Team Dot Apr 14th, 2023
10 min read
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The digital evolution of life sciences has opened the door to the implementation
of advanced technologies. They are already revolutionizing how companies
develop, manufacture and deliver products into the hands of clinicians and
patients.
Today, 45% of life sciences companies’ technology investments are made in three
analytics-related technologies – applied AI, industrialized machine learning
(ML), and cloud and edge computing.
(McKinsey, Jan 2023) Among MedTech executives surveyed, 91% believe emerging
technologies, including cloud and AI, will have a positive impact on their
organizations. In a survey of BioPharma leaders, 75% said they plan to invest
more in AI in the next five years. (Biopharma digital transformation, Deloitte,
2021)
PREDICTIVE AI
With predictive AI, the system uses a manufacturer’s historical data to predict
what might happen in the future. For example, algorithms can be trained using
historical data to predict when a piece of manufacturing equipment is at risk
for failing. In the past, a specific series of events led to the machinery’s
failure.
The AI application detects that these events are happening again, makes the
correlation, and alerts the system user to this trend.
This individual can intervene to prevent a production disruption or the risk
that poor-quality products are produced.
The AI makes the prediction, and the user can act on the information and change
reality if they deem the prediction accurate.
GENERATIVE AI
With the introduction of ChatGPT, generative AI (aka ChatBots) has been launched
into the spotlight and companies across many industries are working to determine
whether they can leverage it to drive value. Falling under the category of
machine learning (ML), generative AI is a category of algorithms that generate
content (e.g., text, images, audio, video, code) based on existing data sources.
So, unlike predictive AI, which observes and classifies patterns in data,
generative AI creates something new from existing data sources.
For example, a user could ask a generative AI model trained on FDA life sciences
regulations, “what guidance has the FDA issued with regards to the use of AI in
drug development” and the model would return a summary based on the information
on which it was trained.
Some life sciences companies have begun experimenting with generative AI to
inform research and development (R&D) efforts. For example, Janssen
Pharmaceutical, a drug company owned by Johnson & Johnson, is testing Syntegra’s
AI generated “synthetic patient data” to “to synthesize a patient journey more
adequately” in the absence of real-world data (RWE).
What the near future holds for AI use in quality management systems (QMS)
Generative AI technologies, particularly ChatGBT, are generating tremendous buzz
among companies, consumers and the media. While ChatGPT might appear on the
surface to be a novelty, use of generative AI holds true potential in life
sciences quality management. When leveraged strategically and with focus in an
eQMS, it can rapidly deliver into the hands of quality professionals targeted
information derived from the analysis of internal and external data.
As eQMS solutions have evolved over the years, the data generated and captured
has exploded in volume and scope.
A decade ago, a quality management professional with a highly manual and
disconnected quality management system might have had access to a handful of
data points on their company’s product development or manufacturing processes.
Today, a professional with a fully integrated, enterprise-wide cloud based eQMS
with digital processes and data likely has hundreds of data points. This volume
of data will continue to grow exponentially as technology continues to evolve.
This creates a huge challenge for the life sciences industry. With more data
comes more responsibility. A quality manager with one data point they must
analyze to assess its impact on product quality and safety to comply with
regulations likely has the time and resources to do perform this task. But a
manager faced with 100 data points, all requiring investigation, creates a
tremendous problem for the company in the modern quality and compliance space.
Generative AI has the potential to provide quality management professionals the
power to investigate the impact of data points and trends with the speed and
accuracy required in today’s business and regulatory environment. They can sift
through the signs to determine where to act based on the nature of the
problem/quality issue, and exactly what type of action is required.
Generative AI within an enterprise-wide cloud-based eQMS also enables the
quality team to communicate recommended actions to all relevant stakeholders,
both internal functions (e.g., R&D, manufacturing, distribution) and outside
suppliers. It is the next phase of quality management – real-time information
and actionable insights for making strategic decisions with unprecedented speed.
LEARN MORE ABOUT AI FOR THE LIFE SCIENCE INDUSTRY HERE.
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