• CKD (Chronic Kidney Disease): A long-term condition where the kidneys don’t filter blood as well as they should.
• ESRD (End-Stage Renal Disease): The final stage of CKD, where the kidneys can no longer function on their own to support life.
• GFR (Glomerular Filtration Rate): A test used to check how well the kidneys are working by estimating how much blood passes through the tiny filters (glomeruli) each minute.
• Nephron: The functional unit of the kidney that actually does the work of filtering blood and producing urine.

Patients often describe symptoms using non-medical, experiential language. When interpreters render these descriptions into clinically relevant terminology, they must do so carefully and accurately, ensuring that the patient’s meaning is preserved without adding, omitting, or altering clinical content.

Interpreters play a key role in medical settings by faithfully conveying what patients say, without adding labels or making assumptions about causes. This allows physicians to assess the clinical significance themselves.

For example:

• Edema: A patient may say, “My legs are puffy like dough.” An interpreter can accurately report this as the patient experiencing leg swelling, without assigning a cause.
• Hematuria: A patient might report “tea-colored urine.” An interpreter can convey this as the patient noticing discolored urine, without suggesting a diagnosis.
• Reduced or absent urine output: If a patient says, “I haven’t gone to the bathroom in a day,” an interpreter can report this as the patient experiencing little or no urine output, leaving the clinical interpretation to the physician.

Nephrology also contains terms that sound similar but have very different meanings. Dropping a prefix or misinterpreting an abbreviation can change the understanding of whether a condition is sudden or gradual, reversible or permanent.

ARF vs. CRF: ARF (Acute Renal Failure) is a sudden loss of kidney function that may be reversible, while CRF (Chronic Renal Failure) is a gradual, often permanent decline. Interpreters can relay these terms as presented by the physician, without implying urgency or prognosis.

• Proteinuria: If a patient is told they have protein in their urine, an interpreter can convey this exactly as stated, without implying cause, so the physician can discuss the significance.
• Dialysis: When a physician explains dialysis — hemodialysis (blood filtered through a machine) or peritoneal dialysis (using the lining of the abdomen) an interpreter can accurately relay the procedure and method as described.

Providing only a partial summary or “the gist” of a kidney exam can risk miscommunication. By accurately conveying distinctions such as intrarenal (inside the kidney) versus prerenal (related to blood flow to the kidney), interpreters ensure patients receive the full information needed to understand their condition, while leaving clinical judgment to the physician.

We often present clients with guidance on how to work with interpreters, and frequently get asked about AI. This is because many people are waiting for the day that they can simply go online and use AI to seamlessly translate between two different languages, but we would like to say it out loud here: THAT DAY HAS YET TO COME.

Please also check out this flip-book we’ve made https://heyzine.com/flip-book/20de67a12a.html

Please also check out our playlist for Chinese localization case studies: https://www.youtube.com/playlist?list=PLO-QGEbwcTr14xqfiR38Mp-EhHAmclsUY

We localized the Interpreters and Music video into traditional Chinese as an example to compare translation accuracy between humans versus AI and to identify some classic AI issues.

One of the biggest weaknesses of AI is that it often struggles with names. For instance, the name “Laura” was translated into both “蘿拉” and “勞拉.” When we saw this inconsistency in names, we looked at each other with amusement because this happens all the time. Some may say AI spelling names incorrectly isn’t a big deal since it’s an easy fix. However, for those people, we’d like to share a real-life example.

In a lease contract we worked on, Paragraph 1 said that the landlord shall be known as ‘A’ and the tenant as ‘B’. Paragraph 2 called the landlord ‘C’ and the tenant ‘D’. This was a document with 30,000 words that a client asked us to quote for reviewing the translation, which had probably been done by an AI. Just in terms of reviewing names, how much effort would it take to find out if there were places that call the landlord “E” and the tenant “F” and so on? Not to mention all the work it would take to find other mistakes that humans typically need several rounds of review to detect.

Our analysis also uncovered that AI defaults to using the pronoun “你,” referring to males and offering no female form “妳.”

AI have translated love song titles like “Suddenly Missing You” and “Stuck on You” into traditional Chinese, using the male form. The male singers may not prefer using the male form of “you” in their love song titles. Otherwise, a native speaker in traditional Chinese would feel kind of strange, reading it.

We inserted line breaks on messages that appear in the video. With line breaks, AI seemed to lose the context of the lines.

Line breaks are important. We are often requested to insert line breaks in Asian language marketing materials. Take Japanese line breaks as an example. There are some basic rules for line breaks or how to break words up, but at the same time, there are a lot of exceptions, which humans can easily catch if they understand Japanese, but not AI. In other words, humans break things apart (debriefing) and put them together in a creative way, which AI is just not capable of.

It turns out that AI struggles to translate any segment accurately and, at times, produces unnatural and contextually absurd translations. As shown in the screenshot below, even with a relatively short source text, the quality of AI translation was unbelievably subpar.

AI translated “interpretation” as “explanation” due to a lack of context.
AI translated “Performance” to machine’s performance rather than that of the interpreter’s.
AI mistakenly translated the meaning of “like” as “to be fond of” instead of “similar to.”
AI word-for-word translation for “big heart” doesn’t make sense to a Chinese audience.

It’s clear to us that AI is not able to handle messages that are broken down by line breaks. This then leads us to a question: How well could AI handle entire messages without line breaks?

We conducted a retest by removing all the line breaks on messages. In this attempt, the text was formatted in a more machine-friendly way to enhance AI’s understanding. But even so, post-editing remained an essential step, with 80% of the segments requiring significant human intervention. Without this crucial step, AI translations either come across as rigid and less relatable to our audience, or contain mistranslations. Below are some examples.

Example 1:
The AI translation appears rather stiff because the word “sync” was translated literally. The audience might wonder what it means to “sync” one language to another. Human translators are able to further explain the context of sync, that is, interpreters “listen to one language and convey it in another language.”

Example 2:
AI translated “more emotionally acute” as “more impatient,” which not only deviates from the intended meaning of the source, but also negates the impact of the word “music”. During post-editing, we replaced it with “more emotionally sensitive,” which is more contextually accurate.

Example 3:
AI did word-for-word translation again. It doesn’t sound like what a normal person would say in Chinese. As a dynamic language, Chinese favors verbs over nouns and usually keeps sentences short. Therefore, in post-editing, we restructured the sentence to make it fit a typical Chinese writing style, and flow more naturally.

Example 4:
AI’s translation of “concentration” lacked clarity. Without referring to the source, it was hard to grasp the intended meaning. So, we opted for a more precise choice of words.

Example 5:
AI does a literal translation, full of ambiguity and rigidity, which doesn’t make clear sense to a Chinese audience.

Translation is supposed to flow naturally to engage the audience. It is the more immersive and relatable experiences that make humans feel comfortable. These are exactly the areas where we as interpreters and translators can contribute to.

There may be a lot of gloom and doom from some in the community who think that their jobs are at risk, however, the reality is that we’re training AI to speak our language, but they aren’t able to fully understand it like we can. They can process it, try and find the corresponding pattern in their database, and come to a conclusion that they think is right, but they won’t always be. That’s where interpreters and translators will always have the edge over AI. Human creativity and our ability to understand what’s important, and the culture embedded in it, enables us to make sure that we are conveying the intended message.

We tried one of the latest AI platforms to translate one of our office videos into Mandarin.

While we were impressed by the seamless process and the voice cloning feature that enhanced voice modulation, we couldn’t help but notice pronunciation and translation errors in the generated video. Given that Mandarin Chinese is a tonal language, tones can become a source of misunderstanding if not pronounced correctly. The chosen video introduces the rental service of our conference room, making “conference” a high-frequency word. However, throughout the video, AI consistently pronounces the Chinese word for “conference,” as “memory,” with tones differing from the former. Also, “state-of-the-art” in Chinese is pronounced the same way that “cash” is. This could undoubtedly complicate the message we aim to convey if left alone.

The translation issues we caught are mostly recurring problems caused by machine translation as discussed above. Take the first sentence as an example. AI translated “Looking for a conference room to have a meeting over video or in person?” as “Can you look for a conference room via video or in person meeting?” AI’s rendition deviates from the original meaning, which is likely caused by line breaks, leading to confusion and miscommunication. Such discrepancies underscore the importance of post-editing and human intervention to refine machine-generated translations.

Our conclusion becomes clear.

In this age of AI becoming more prevalent, humans just need to work smarter to beat out AI. As individuals in an evolving world, it’s important to accept technological advancements, but also understand that AI lacks creativity, individuality, improvisation capability, and the understanding of human cultures. That’s how humans can break through and go beyond AI’s limitations.