Introduction
Many clinical trials these days are global in nature. Patients recruited into the trials, as well as the researchers conducting the trial in their country of origin, usually do not rely on English as their primary language. As such, the importance of translating and localizing clinical trials, so the data can be gathered, researched, and made available across borders is now a vital necessity.
Read our recent blog on Making Medical Translation A Differentiating Factor For Successful Clinical Trials.
There have been many technology-related advancements in the medical translation industry over the past few years, including Artificial Intelligence (AI), Voice Recognition Technology and even multi-lingual Chatbots. The goal of this blog is to highlight these key advancements and how they work.
The Early Days Of Medical Translation
It wasn’t that long ago (pre-Google era) when translation requests arrived by fax, and many translators dictated their texts onto micro-cassettes. They then physically went to nearby medical libraries to track down obscure terminology. Translation was a manual and time-consuming process.
1) In other words, human translators were the only option, with linguistic expertise to convert text from one language to another. It was a slow process, prone to errors and subjectivity based on the translator’s knowledge and technical contextual understanding needed for the country of origin.
2) Fast forward twenty-five years, and medical textbooks were replaced by Google, paper dictionaries were replaced by on-line ones, printed glossaries were replaced by computerized terminology records, and paper files were converted to electronic archives. Going to the ‘library’ for a medical translator now meant opening their computers.
Historical Automation Milestones
According to Techie Buzz, it was in the 1960’s that researchers begin using algorithms and computational power to enhance translation processes:
1) Among these developments was the creation of the Automatic Language Processing Advisory Committee (ALPAC) by the US government in 1964
2) ALPAC’s goal was to evaluate the feasibility of machine translation, moving towards a more automated and less manual approach
3) In 1978, the first commercial machine translation system SYSTAN was introduced
4) It marked a significant milestone in the journey towards automated translation
However, the quality of translations was still far from perfect.
Technology-Related Innovations In Medical Translations
The early automation successes set the stage for vast changes in the way medical translations are completed today. Let’s look at three major breakthrough’s that have happened since the advent of SYSTAN:
Artificial Intelligence
Artificial Intelligence (AI) in medical translations involves the use of automated systems to accurately convert medical information from one language to another.
Read our recent blog on the Comparison Between AI-enabled Translation And AI Human-Assisted Translation In Clinical Trials.
By leveraging sophisticated algorithms and extensive databases of medical terminology, AI provides real-time translations that facilitate precise communication between healthcare organizations and patients. According to an excerpt from Machine Translations, “AI plays a crucial role during clinical trials by ensuring accurate and timely communication of essential trial information, patient consent forms, and detailed instructions. This technology guarantees that participants have a comprehensive understanding of the trial processes, which significantly enhances their compliance with the procedures and promotes their overall safety throughout the trial”.
Voice Recognition
Voice recognition technology enables real-time verbal translations, breaking down language barriers in multi-national clinical trial settings. Voice recognition software can understand human speech and convert it into readable text. Clinical language translation voice recognition software follows this same speech-to-text translation process, leveraging a database of medical descriptions and technical terminology unique to that country.
1) Voice recognition software that is able to identify a clinician’s voice and a patient’s voice can more accurately record the spoken interaction between the two.
2) When combined with AI-based natural language processing, complex medical translations requiring technical understanding and cultural nuances can be expeditiously completed.
Multi-Lingual Chatbots
A multi-lingual Chatbot is a conversational interface equipped with AI, which is capable of understanding, processing and responding to user queries in multiple languages. Traditional chatbots, commonly seen on a majority of medical and clinical websites, are constrained to a conversation in a specific language. This updated technology provides automated support in multiple languages, a critical requirement for multi-national clinical trials.
Conclusion
AI-optimized innovations in the medical translations industry have come a long way since the early days of automation. They hold the promise of speed and consistency, while minimizing cost.
Read our recent blog “Top Three Pros And Cons of using AI In Medical Translations”.
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