Alzheimer's disease (AD) causes alterations in speech and language across multiple domains, including semantic content, pragmatics, prosody, morphosyntax, phonology and timing/fluency, with some of these changes detectable early in the disease course. Definitive biomarker assessments of AD are often invasive or resource-intensive; there is a need for scalable measures in screening and monitoring AD. Advances in natural language processing and automated transcription can quantify speech and language features from structured tasks and natural conversations. This review summarizes domain-specific changes across the AD continuum, evaluates evidence linking these features to biomarkers of AD pathology (amyloid and tau), and discusses opportunities and limitations for future work. We conclude by identifying priorities for the field, namely, domain-aware task design, demographic and language-sensitive norms and standardized, biomarker-validated datasets.
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