Background: Modern prodromal Alzheimer’s disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed. Objective: Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations. Methods: Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N = 121) and a Swedish birth cohort (N = 404). MCI classification was each evaluated on the training cohort as well as across on the unrelated validation cohort. Results: The algorithms achieved a performance of AUC  0.73 and AUC  0.77 in the respective training cohorts and AUC  0.81 in the unseen validation cohort. Conclusion: The results indicate that a ki:e SB-C based algorithm robustly detects MCI across different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care.

<b><i>Introduction:</i></b> Progressive cognitive decline is the cardinal behavioral symptom in most dementia-causing diseases such as Alzheimer’s disease. While most well-established measures for cognition might not fit tomorrow’s decentralized remote clinical trials, digital cognitive assessments will gain importance. We present the evaluation of a novel digital speech biomarker for cognition (SB-C) following the Digital Medicine Society’s V3 framework: verification, analytical validation, and clinical validation. <b><i>Methods:</i></b> Evaluation was done in two independent clinical samples: the Dutch DeepSpA (<i>N</i> = 69 subjective cognitive impairment [SCI], <i>N</i> = 52 mild cognitive impairment [MCI], and <i>N</i> = 13 dementia) and the Scottish SPeAk datasets (<i>N</i> = 25, healthy controls). For validation, two anchor scores were used: the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) scale. <b><i>Results:</i></b> <i>Verification</i>: The SB-C could be reliably extracted for both languages using an automatic speech processing pipeline. <i>Analytical Validation</i>: In both languages, the SB-C was strongly correlated with MMSE scores. <i>Clinical Validation:</i> The SB-C significantly differed between clinical groups (including MCI and dementia), was strongly correlated with the CDR, and could track the clinically meaningful decline. <b><i>Conclusion:</i></b> Our results suggest that the ki:e SB-C is an objective, scalable, and reliable indicator of cognitive decline, fit for purpose as a remote assessment in clinical early dementia trials.