For decades, many of those ostensible science fiction movies gracing the big screen have included some version of artificial intelligence (AI). In the cinematic world, the AI entities commonly exhibited human characteristics and personalities, which was often what the movie plot was built around. In reality, AI is designed to simulate human intelligence processes. Using algorithms, the technology acquires information, determines how to analyze and use the information, and self-correct upon receiving new data — exponentially faster than humans.
AI, in concert with its associated technologies, machine learning (ML) and natural language processing (NLP), are reimagining various aspects of the life sciences industry. The influx of big tech firms collaborating with incumbent life sciences companies to develop AI-based medical devices indicates that this trend will continue at a rapid pace. Tech giant Google recently inked a partnership with the Mayo Clinic to develop AI and ML tools for cancer treatment and to implement the technology to help mitigate the clinic’s processing bottlenecks.1
The following are just a few applications of this modernized technology in health care:
AI technologies are highly useful with predictive analytics, which involves the use of current and historical data to make more accurate predictions about future or otherwise unknown events. This is possible due to the technology’s ability to quickly find data that directly relates to a behavior or issue. Having this information on hand is invaluable, which likely contributes to the technology’s popularity. The growing adoption of data-driven decision-making is putting the health care analytics market on a growth trajectory forecasted to reach $50.5 billion by 2024.5
Life sciences companies are also using these technologies as a tactical advantage in achieving compliance. Employing AI technologies and data analytics strategies, companies can get a comprehensive view of quality management in real time, making it possible to:
Accurately identifying and mitigating new or potential compliance risks helps ensure quality at each phase of product design, development, and manufacturing. For instance, the most challenging and time-consuming tasks in working through a deviation or nonconformance scenario is root cause analysis. This commonly involves the use of a tracking and trending system to identify similar occurrences or deviations that are part of a larger risk.
Manual tracking systems and processes can be arduous and lack the ability to effectively classify root causes and similarities. Quality deficiencies are often discovered that should have been identified much earlier or should not even exist. Consequently, quality professionals spend much of their time addressing quality issues and putting out fires, instead of focusing on quality improvement and other critical business functions.
Organizationally, modernized technologies can drive further productivity advances in business processes. The companies that emerge as leaders are those that seek opportunities to advance their platform plays and find the next generation of solutions. According to survey data from technology consulting firm Accenture, by 2022:6
Going forward, AI will favorably impact a wider spectrum of the organizational culture. There will be new paradigms in education and skills development, human creativity will flourish, and workplaces will reestablish collaboration, which is sorely lacking in today’s workforce.
View the trend brief “Shaping the Next Normal for Quality and Compliance” to learn more about AI and other trends shaping quality and compliance in 2021.
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