De-identification of clinical data: A systematic review of free text, image and tabular data approaches

Abstract

De-identification techniques have evolved, with increased use of Language Models and a decline in recurrence-based neural networks. Off-the-shelf tools often require customisation for optimal performance. Most studies de-identify English content, supported by the prevalence of English datasets. Key challenges include the phenomenon of code-mixing (i.e., more than one language used in the same sentence) and the scarcity of available datasets for reproducibility.

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