Webinar Recap: Life Sciences and Generative AI

Though generative AI tools may feel futuristic, they have roots in the 1800s. We’ve been preparing and exploring how these tools, including ChatGPT, can be applied to Lionbridge’s Life Sciences translation services.  Learn more from Paraic O’Donnell, a Life Sciences industry veteran, and Vincent Henderson, our lead Generative AI expert, about how we currently apply Generative AI for our Life Sciences clients and some future use cases.

If you missed our webinar on Life Sciences and Generative AI, you can watch it on demand.

Don’t have time to watch the webinar? Read about key points our team addressed during the webinar.

GenAI’s History: More than Just a Risky New Technology

People in the life sciences industry are naturally risk-averse. They bear the responsibilities of patients’ lives and wellness and thus aren’t often first adopters of new technology and fads. Though Generative AI solutions are new in many ways, their roots can be traced back to Charles Babbage’s difference engine in 1833 and to Alan Turing’s later vision of machines that can “learn from experience” and alter and improve their instructions.

More concretely, Lionbridge has been using Machine Translation, particularly Neural MT (NMT), for years to help our Life Sciences Customers communicate better and faster with patients, regulatory bodies, vendors, etc. We’re prepared to use, develop, and trust generative AI solutions for our Life Sciences customers because we deeply understand them.

Life Sciences and Generative AI: What Can Large Language Models (LLMs) Do for the Industry?

LLMs (the basis for generative AI tools) have abilities well-suited to many tasks, including the language needs of life sciences organizations. LLMs are trained on the internet, a vast body of language encompassing much of human knowledge. Due to their neural network architecture, LLMs can engage in “deep learning,” thus building the ability to infer patterns and significance with little human assistance. They can also modify the structure or style of their output, self-correct, and classify things. These tools can also interact with human operators fluently and naturally. Humans can use prompts and questions to get complete answers, similar to what another human would provide. These abilities lend themselves to many life sciences-specific challenges.

Though AI tools aren’t yet performing these tasks outside of test environments, their abilities suggest they will, especially when the technology can perform at scale like their NMT predecessor.

Life Sciences and Generative AI Current Use Cases

Language service providers like Lionbridge are already using AI-powered solutions to assist our customers because of our extensive experience with NMT.

With AI’s enhanced abilities, we can achieve these activities to an even greater degree with less human supervision. Overall, translation and content services companies like Lionbridge use AI to help our customers reach their patients and markets (even new markets) faster and develop content more efficiently based on existing information reservoirs. We’re also ensuring content quality and compliance. These are all critical tasks for most Life Sciences organizations that must meet regulatory standards.

Future Life Sciences and Generative AI Use Cases

The current Life Science applications for AI will grow exponentially in the future. The early gains will be in lower-risk content classes, such as marketing materials. However, as we build trust in AI technology and new ways to ensure trustworthy generative AI content, we’ll be able to expand applications to these areas:

• Multinative Content: Generating new corporate and marketing content for new multilingual markets.

• Lessening AI Oversight and Training Burdens: As the Internet’s database of scientific knowledge grows, AI tools will become more and more competent and informed.

• Context Awareness: Instructions for content creation, localization, or translation can be retained and expanded, thus enabling iterative refinement outcomes.

• Few-shot Learning: Combined with context awareness, LLM design will enable rapid assimilation of new directives.

• Clinical and Regulatory Workflows: AI tools could make previously labor-intensive data migration easier and update propagation.

• Compliance and Patient Safety: AI can help maintain these vital standards with human guidance and safeguards.

It’s critical to note that AI will never displace human Life Sciences experts. It will only help these experts to become more efficient and focus on areas where their expertise is more needed.

Get in touch

Interested in using Generative AI for your patient communications, clinical research, or marketing materials? Lionbridge has decades of experience and expertise serving some of the largest Life Sciences companies. We know best practices for medical device translation services, clinical trial translation, and more. Contact us today to find out how Lionbridge can help as your language service provider.