In the last decade, a rapidly growing body of studies has shown promising results for the automatic detection and extraction of speech and language features as biomarkers of neurodegenerative conditions such as Alzheimer’s disease. This has sparked great optimism and the development of various digital health tools, but also warnings regarding the predominance of English in the field and calls for linguistically diverse research as well as global, equitable access to novel clinical instruments. To automatically extract clinically relevant features from transcripts in low-resource languages, two approaches are possible: 1) utilizing a limited range of language-specific tools or 2) translating text to English and then extracting the features. We evaluate these approaches for part-of-speech (POS) rates in transcripts of recorded picture descriptions from a cross-sectional study of Icelandic speakers at different stages of Alzheimer’s disease and healthy controls. While the translation method merits further exploration, only a subset of the POS categories show a promising correspondence to the direct extraction from the Icelandic transcripts in our results, indicating that the translation method has to be linguistically validated at the individual POS category level.