The Role of AI in Life Sciences Localization 

The integration of AI into the localization process marks a significant advancement, albeit one that is often met with skepticism. The 6th blog in our Life Sciences Localization series aims to shed light on the pivotal role of AI in life sciences localization and outline best practices for its integration. This ultimately enables reduced time to market while maintaining quality levels and regulatory compliance.

Manual to MT Evolution

The Pre-AI Era

Historically, translation tasks were performed manually, without the aid of computers, a method that was both time-consuming and prone to inconsistencies. The 1990s heralded a new era with the introduction of Computer-Assisted Translation (CAT) tools, revolutionizing the translation process by enhancing both efficiency and consistency. 

Advent of Machine Translation 

The localization industry swiftly embraced AI, particularly through Machine Translation (MT). Statistical MT gained mainstream acceptance around 2013, despite the challenges it posed for translators, notably the awkwardness of the outputs which made post-editing arduous. By 2015-2016, Neural MT emerged, boasting the capability to generate often fluent translations, albeit still susceptible to errors in style, terminology, and accuracy. 

Incorporating neural MT into translation workflows, particularly for life sciences, signifies a monumental leap forward. While certain documents may be suitable for automated MT, critical materials require a meticulous QC process to ensure the highest standards are met. 

The Emergence of Generative AI 

Generative AI represents the next frontier in localization. Despite their propensity for producing errors or “hallucinations,” Large Language Models (LLMs) offer numerous applications to streamline various aspects of the translation process, enhancing both productivity and quality. 

Applications of Generative AI in Life Sciences Localization include: 

• AI-augmented quality assessment and correction, exemplified by metrics such as #GEMBA
• Language editing tasks, such as sentence shortening and style adaptation
• Creating multilingual term bases
• AI-assisted consistency checks across multiple document types
• Research on terminology and content

The LLMs must comply with stringent data security standards, in order to harness the capabilities of AI responsibly.

There is a lot of discussion around using Generative AI to do multilingual content creation in place of translation. The level of consistency that is required for life sciences between the source and target languages creates a very challenging use case for this new approach, which needs to be explored further over time.

Conclusion 

The integration of AI into life sciences localization is not just a trend but a transformational shift that promises to redefine how information is shared globally. As we move forward, the role of AI in overcoming language barriers and fostering global health literacy will undoubtedly continue to evolve and expand, marking a new era of accessibility and understanding in the life sciences sector. 

Check out my upcoming classes with the #localizationinstitute:

• ReadySetGo: Life Sciences Localization: April 24, 2024: Participants will learn about the regulatory requirements impacting the localization process and best practices to effectively achieve compliance with these requirements.
• Life Sciences Localization Master Class: May 1-22, 2024: This course will enable participants to understand what localizing life sciences content entails and how it can be effectively managed in a life sciences regulatory context. This class will focus on how to incorporate process and technology to effectively achieve compliance with these requirements.

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