OBJECTIVES: As technology continues to advance and the need to expedite drug development increases, the applications of Machine Translation (MT) and Artificial Intelligence (AI) in translations should be further explored. The capabilities of iterative learning and feedback can serve as a tool in the translation process, but the limitations should be considered. This poster will compare human and machine translation in the back translation step for Clinical Outcome Assessments (COAs) as well as supporting study materials.

METHODS: A side-by-side comparison of two translation methods, human vs. machine, was performed. The comparison was performed specifically on un-edited, single-step back translations. The analysis categorized the findings based on similarities, differences, and any errors found.

RESULTS: The review of each method of back translation demonstrated that machine translation is comparable to the human translation. Conceptual equivalency is found in the machine translation as well as the human translation. Errors were also found in both methods.

CONCLUSIONS: Machine translation may be considered as a future option in the translation process. Although there are cautionary points to consider, iterative learning can provide improvement in quality and reduce time friction that is often seen in the traditional translation process. Further analysis may be necessary to determine the time savings associated with the use of MT and AI in this and other linguistic steps.