FEATURE
One colossal challenge looms in the pharmaceutical industry’s relentless data landscape: the sheer volume of information. Amid the chaos of documents and decisions, busy teams often get bogged down. Enter GenAI, the solution they’ve been waiting for. “GenAI can accelerate our time to delivery in our core business processes,” shares Jeevaka Kiriella, director of global data science at Merck. “One fundamental way to do that is by leveraging its generative aspect as a starting point or foundation. For instance, in the realm of clinical trial operations, one use case involves the creation of protocols and related study documents. In this upstream activity, input to a large language model (LLM), the LLM can generate initial protocols This should decrease the time needed to create and revise such documents, ultimately expediting the initiation of a trial.” and increase product yield. “GenAI allows us to access the entire pharmaceutical supply chain, including research, trials, manufacturing, and Kiriella also sees GenAI helping to improve product availability commercialization. This enables faster decision-making, ultimately leading to improved product availability. The pharmaceutical industry is actively investing in computational drug discovery powered by GenAI, in research.”
we can further optimize manufacturing processes using interactive LLMs to identify bottlenecks and enhance product yield. Additionally, in the commercial sphere, foundational models can be utilized for tasks such as target materials, and gaining assisted access to primary market research insights.” As individual organizations identify the use cases that best suit their business, GenAI’s transformative effects are already extending across the industry. leap forward in applying AI/ML with the adoption of foundational models,” Kiriella reveals. “This shift allows for a drastic reduction in the time needed to develop AI applications, leading to release of LLMs, AI is now accessible to
users throughout the company. In the pharmaceutical sector, we have a unique opportunity to harness this potential due to our investments in data and analytics.” considerations that will help pharma companies ensure long-term success with the technology. “Organizations should promptly assess and address the potential impact of GenAI. In addition, they must responsively meet the this may involve securely integrating commercial foundational models to mitigate compliance risks or effectively processing large amounts of internal data to drive foundational to remain open to different models, leveraging the most suitable one for each use case.”
large foundational models is clinical operations. By optimizing this process, we can swiftly evaluate viable
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ai:sight by Fractal Volume 7
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