Big data and artificial intelligence in post-stroke aphasia: A mapping review

Abstract

BACKGROUND: Aphasia is an impairment of language as a result of brain damage which can affect individuals after a stroke. Recent research in aphasia has highlighted new technologies and techniques that fall under the umbrella of big data and artificial intelligence (AI). OBJECTIVES: This review aims to examine the extent, range and nature of available research on big data and AI relating to aphasia post stroke. METHODS: A mapping review is the most appropriate format for reviewing the evidence on a broad and emerging topic such as big data and AI in post-stroke aphasia. Following a systematic search of online databases and a two-stage screening process, data was extracted from the included studies. This analysis process included grouping the research into inductively created categories as the different areas within the research topic became apparent. RESULTS: Seventy-two studies were included in the review. The results showed an emergent body of research made up of meta-analyses and quasi-experimental studies falling into defined categories within big data and AI in post-stroke aphasia. The two largest categories were automation, including automated assessment and diagnosis as well as automatic speech recognition, and prediction and association, largely through symptom-lesion mapping and meta-analysis. CONCLUSIONS: The framework of categories within the research field of big data and AI in post-stroke aphasia suggest this broad topic has the potential to make an increasing contribution to aphasia research. Further research is needed to evaluate the specific areas within big data and AI in aphasia in terms of efficacy and accuracy within defined categories.