Abstract
MCID (minimal clinically important difference) is a patient-centered concept used in clinical research that represents the smallest change that someone living with Alzheimer's disease would identify as important. There are several challenges associated with the universal application of this construct. Alzheimer's disease progresses differently for each individual, complicating the establishment of a universal standard that accounts for individual-level issues. Alzheimer's disease is also a gradual and evolving disorder, and what is perceived as clinically meaningful can vary significantly at early and late disease stages. People living with Alzheimer's disease and caregivers may have differing perspectives on the benefits of treatment outcomes, making it more challenging to establish an appropriate MCID. Moreover, Alzheimer's trials rely on a variety of tests to evaluate cognitive and functional impairments. However, these tests often lack sensitivity to early-stage changes and are affected by variability in rater rankings. Digital biomarkers and advanced health technologies have emerged as a hot topic in modern medicine. They offer a promising approach for detecting real-time, objective clinical differences and improving patient outcomes by enabling continuous monitoring, individualized assessments, and leveraging artificial intelligence learning for complex analytical predictions. However, while these advancements hold great potential, they also raise important considerations around standardization, accuracy, and integration into current clinical frameworks. As new technologies are introduced alongside evolving regulatory frameworks, the primary focus must remain on outcomes that truly matter to people living with Alzheimer's disease and their caregivers, ensuring that the principle of clinical meaningfulness is not lost. HIGHLIGHTS: Minimal clinically important difference (MCID) represents the smallest change in a patient's condition that would be considered meaningful, but defining this for Alzheimer's disease is challenging due to its heterogeneity.The perception of what is clinically meaningful may differ at the individual level, at different disease stages within the same individual, and between patient and caregiver.Traditional tests used as endpoints in Alzheimer's trials lack the sensitivity to detect subtle changes and are limited by range restrictions, making them less effective for accurately capturing treatment efficacy.Digital biomarkers and artificial intelligence (AI)-driven health technologies may offer the potential to enhance the detection of clinically meaningful changes by providing continuous, objective monitoring and advanced analytics for individualized patient assessments.Both the United States Food and Drug Administration (FDA) and European Medicines Agency (EMA) are playing pivotal roles in advancing the use of digital health technologies, facilitating the evolution of regulatory frameworks to ensure these innovations are effectively integrated into clinical research and practice.