Human vs. Machine?
Harmonizing Human
Expertise with AI
Efficiency in Life Sciences
The stakes are always high for localization in the life sciences industry. However, balancing speed, accuracy, and cost is a constant challenge.
While other industries must also balance speed, accuracy, and cost in localizing content, life sciences organizations face unique challenges.
Advancements in AI and neural machine translation (NMT) technologies have significantly improved the quality of machine-generated translations.
Despite AI advancements, human translators remain irreplaceable. Localization requires a nuanced understanding of languages and cultural sensitivity, which AI is not (yet) capable of.
The decision between machine and human translation hinges on balancing speed, accuracy, and cost.
The landscape of translation technologies is constantly evolving. Advancements in AI, machine learning, and NMT will continue to refine the quality and capabilities of machine translation.
Introduction
The stakes are always high for localization in the life sciences industry. Translating vital information across multiple languages should meet three requirements: patient engagement, regulatory compliance, and timely communication. However, balancing speed, accuracy, and cost is a constant challenge.
Language service providers (LSPs) have long relied on machine translation (MT) and artificial intelligence (AI) to deliver large volumes of translated content. The advent of Generative AI (GenAI) and large language models (LLMs) has raised expectations for faster content generation and translation, even at the early stages of localization workflows.
Despite these advancements, the quality of unedited MT still falls short of human translation. This leaves LSPs and their clients with a crucial decision: prioritizing AI efficiency or human expertise in their translation.