What does Linguistic AI mean for most medical device organizations?
Sacrificing quality for speed?One of the biggest perceived drawbacks to implementing a machine-first approach is the quality of the translation – a machine can’t “talk” like a human. Machines are trained. When we hear the term “machine learning” – we think of unsupervised processes where machines get better without any human intervention. That’s only partially true. Machines learn from data and humans have to either kick-start the process or monitor the process such that bias isn’t introduced into the system. By combining Linguistic AI with human post-editing capabilities, the expertise and skillset of the translators ensure that sensitive, life-saving medical content is accurate, respects guidelines and other linguistic preferences.
How secure is Linguistic AI?
Medical device manufacturers may also have security risk and data breach concerns due to the sensitive data these organizations handle. Unfortunately they should be concerned, as online machine translation tools do not provide adequate data security and tailoring controls for the specific needs of medical device organizations. Furthermore once something is translated using these online translation tools that content is potentially available in the public domain. If data privacy and security is a top priority, you should only be implementing enterprise grade solutions with firewall protections.
Not all Linguistic AI use cases are equal
The quality, integrity and volume of data will pretty much dictate if Linguistic AI is a viable approach for your specific use case, completing a detailed analysis should be one of your first steps. Additionally certain areas of content given their structure tend to lend themselves better to this approach, however looking beyond the usual suspects (online help, etc.) would be a wise decision, as we have seen customer success across areas as diverse as labeling and safety.
SDL Linguistic AI ™ (Hai) is a technology that powers SDL’s content management and language solutions, and helps to process, understand and generate content, by finding patterns and connections within content across languages. By applying AI to content, we can understand its structure, language and intent which enables us to automate and scale content processes such as translation. By combining Linguistic AI with post-editing capabilities translations can processed faster at less cost while maintaining quality. It’s technologies like these that companies should explore if they want to address advanced content challenges, and take advantage of the opportunities of the vibrant, exciting intelligent translation era.
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