Alzheimer’s disease (AD) has a major impact on daily functioning and quality of life and is characterised by a huge socio-economic burden, with at least 50 million people affected worldwide. For clinical practice, we need to develop algorithms based on easily accessible and time- and cost-effective tests like blood-based biomarkers and digital cognitive tests for personalised diagnosis, prognosis and correct symptomatic treatment. For clinical trial development, we need to optimise the screening procedure to accurately identify individuals with AD in pre-symptomatic stage or prodromal disease stages and predict progression rates at an individual level. The methods need to be robust and generalizable, and the test results need to be optimally communicated to patients. This initiative will leverage demographically and ethnically diverse population-based, primary care and memory clinical cohorts that are deeply phenotyped and have long-term follow-up data available. Using state-of-the-art machine learning approaches, we will define, validate and implement accessible and cost-effective AD biomarkers for personalised diagnostic and prognostic work-up, and to facilitate development of disease modifying treatments in AD. By revolutionising the diagnostic work-up and improving participant selection and monitoring for clinical trials, the personalised medicine approach developed in EDAP-AD will meet the challenges posited by AD.