The use of digital technologies has become the standard within the life sciences industry, and the digitalization of data has facilitated the adoption of artificial intelligence (AI). The pace of this technology adoption has only continued to accelerate.
The U.S. Food and Drug Administration (FDA) has embraced the use of AI in FDA-regulated products, stating:
“AI solutions have the potential to improve automation and learning of medical devices, the efficiency of diagnostic/therapeutic development and commercial manufacturing, regulatory assessment, and post market surveillance, among many other potential applications.”
Companies that adopt a strategic approach to AI, focusing on practical and proven applications, where AI informs decision-makers rather than making decisions, can expedite time to value.
Let’s consider the evolution of quality processes, the prevalence of AI in life sciences manufacturing and best practices for deploying generative AI in quality.
The Evolution of Quality Processes
Years ago, quality management professionals relied on using manual, paper-based systems for managing their quality processes. Their tasks included documenting quality processes, obtaining signatures from authorized individuals, and storing documents for regulatory compliance.
This manual approach eventually evolved into the next phase—the adoption of on-premises electronic quality management systems (eQMS). While these systems allowed for electronic documentation and storage, they lacked integration with other relevant systems, resulting in manual processes for routing, approvals, information sharing, and data analysis.
Today, many life sciences manufacturers have transitioned to cloud-based digital eQMS solutions that are fully integrated with their other systems. These solutions automate quality management processes, streamline data capture and analysis, and provide visibility across the enterprise.
With advanced analytics capabilities, quality managers can quickly identify and address issues like deviations from standard operating procedures, supplier quality issues, or labeling errors.
AI in Life Sciences Manufacturing
The digital evolution in life sciences has enabled the adoption of advanced technologies, transforming product development, manufacturing, and delivery processes. Digital transformation, combined with AI technologies, does not entail replacing quality managers and other users. Instead, it involves providing them with external AI capabilities to enhance their focus on critical, ongoing tasks.
Predictive AI: Predictive AI uses a manufacturer’s historical data to anticipate future occurrences. For instance, algorithms trained on past data can predict potential equipment failures. When the system detects recurring patterns indicative of machinery failure, it alerts the user, enabling intervention to prevent production disruptions or the production of low-quality products. The AI predicts, and the user can take action based on the prediction’s accuracy.
Generative AI: Generative AI has gained attention since the launch of ChatGPT, prompting companies in various industries to explore its potential value. Falling under machine learning (ML), generative AI comprises algorithms that produce content such as text, images, audio, video, or code based on existing data sources. Unlike predictive AI, which identifies patterns in data, generative AI generates new content from existing data. For instance, a user could query a generative AI model trained on FDA life sciences regulations about AI use in drug development, and the model would provide a summary based on its training data. Some life sciences organizations are experimenting with generative AI to aid research and development (R&D) endeavors. For example, Janssen Pharmaceutical, a Johnson & Johnson subsidiary, is testing Syntegra’s AI-generated “synthetic patient data” to simulate patient journeys in the absence of real-world evidence (RWE).
Best Practices for Deploying Generative AI in Quality
Deploying AI in quality management requires a strategic and focused approach to maximize its value. Life sciences organizations can navigate the complexities of AI deployment while optimizing its impact on quality management processes by considering the following:
- Be Structured and Focused: Life sciences manufacturers facing increased costs and struggling with financial pressures must carefully evaluate AI investments for potential benefits. While many companies have embraced AI to avoid falling behind, investing in generative AI without structure and focus may not yield the desired return on investment (ROI).
According to PWC’s 2024 AI Business Predictions, “many companies will find attractive ROI from GenAI, but only a few will succeed in achieving transformative value from it.”
To succeed, learn from industry best practices, focus on specific use cases, and avoid attempting to implement AI without guidance. Instead, partner with an eQMS solution provider offering generative AI capabilities to navigate the technology effectively and maximize its value in your organization.
- Look for Faster Implementation and Time to Value: A strategic focus should guide your generative AI journey without the need for a lengthy, complex, and costly process. By opting for a cloud eQMS solution equipped with ready-to-use generative AI technology, your quality management team can start leveraging AI into your workflows within weeks.
Choose an eQMS provider with proven life sciences industry expertise and a platform preconfigured to industry standards like FDA, ISO, and GxP. With a ready-to-use solution and expert guidance, you can become an early adopter of generative AI with minimal risk, enabling rapid understanding, initial success, and confident expansion into other areas.
- Embrace the Synergistic Relationship between AI and Humans: There’s been a certain fear around generative AI, with professionals in various industries questioning whether their jobs can be replaced by it. However, generative AI doesn’t have to be human vs robot but rather a synergistic relationship. In heavily regulated fields like life sciences, where quality is paramount, AI should not make decisions but rather support decision-makers.
For example, generative AI algorithms trained on FDA regulations and company procedures can flag potential deviations and suggest steps for resolution and reporting. However, it’s up to quality professionals to apply their expertise and judgment to assess the relevance of this information and determine the appropriate course of action.
Given the FDA’s risk-based approach to quality and oversight of AI, life sciences manufacturers must adopt a similarly risk-based approach to adopting generative AI in their operations, emphasizing human control. Generative AI offers the advantage of accelerating data analysis for quality professionals, empowering them to make informed decisions.
- Tap into the Pool of Industry Wisdom: As the life sciences industry grapples with how to best incorporate generative AI capabilities into their operations, manufacturers with a pre-configured solution are already reaping the benefits from the collective wisdom of other users.
With a ready-to-use eQMS that does not require heavy customization, users across life sciences companies can share common data, processes, and use cases, enabling rapid deployment of generative AI technology. As members of this user community implement the pre-configured generative AI, they collectively enhance its capabilities by sharing feedback and experiences, informing algorithm refinement by the eQMS provider. This collaborative process ensures consistent performance and results across enterprises, expediting the realization of generative AI value for the entire community.
Generative AI Is Transforming the Life Sciences Industry
Organizations within the life sciences industry are rapidly adopting digital technologies, like generative AI, to transform drug and device development, manufacturing, and commercialization. To mitigate risks and optimize ROI, companies should take a strategic approach to the application of these tools.
Selecting an eQMS solutions provider with proven life sciences industry expertise, a ready-to-use platform, and expert guidance can help empower you on this journey.
We’ve only just scratched the surface. Download our white paper, “How Generative AI will Affect the Life Sciences Industry,” for more details and information on navigating this evolving digital landscape.
