Digital Twins in Late-life Neurodegeneration Prevention, Diagnosis, and Care: A State-of-the-Art Review

Abstract

Background & Rationale The rise of digital health technologies presents a transformative opportunity to enhance the clinical care pathway for late-life neurodegeneration diseases including Alzheimer’s, Parkinson’s, and dementia.(1) Among them, digital twins, virtual models of individual patients created from real-world medical and biological data, offer a powerful paradigm for personalized medicine.(2) By integrating multimodal data,(3) digital twins have the potential to track longitudinal changes, predict the rate of decline,(4,5) and simulate intervention outcomes,(6) providing researchers, clinicians, patients, and caregivers with critical insights meant to support treatment choices and care planning.(7,27) Despite their promise, digital twins for late-life neurodegeneration raise several critical challenges. (8,9) The current absence of recognized standards means their design bears the risk of reflecting the values, assumptions, and priorities of their developers, shaping how disease risk, progression, and intervention are framed. (10) Without careful ethical and regulatory oversight, these technologies may have profound implications for patients’ well-being, autonomy, and rights. (11,12) In particular, digital twins for late-life neurodegeneration risk medicalizing social and environmental determinants of brain health by deemphasizing the links between environmental exposures and cognitive decline, effectively shifting responsibility for systemic factors onto individuals.(15,16) Moreover, although early diagnosis and intervention aim to improve clinical outcomes, they also introduce risks of overdiagnosis and potential infringements on bodily autonomy.(13) The ability to predict disease onset, for instance, raises complex ethical and legal questions, especially concerning advance directives and legislation on medical assistance in dying.(14,28) Furthermore, the effectiveness of digital twins depends on their ability to accurately reflect diverse populations.(17) Ethnic, racial, and socioeconomic inequities are well documented in dementia including AD and in PD, affecting diagnosis, treatment access, and clinical trial representation.(18,29) Variations in genetic risk factors, comorbidities, and healthcare accessibility highlight the need for inclusive models that can support equitable healthcare solutions.(19) Ethical safeguards and careful design choices are essential to ensure that digital twins empower individuals and clinicians without perpetuating unintended harms.(13,20) To address these challenges, this project aims to bridge the gap between the technological potential of digital twins and their real-world implementation in late-life neurodegeneration by understanding how key knowledge users, including future patients, caregivers, researchers, and healthcare providers, perceive, accept, and engage with these technologies.(10,21) Objectives & Research Question This review conceptualizes innovation in ADDTs across three dimensions of design thinking: technical feasibility, clinical utility, and socio-ethical desirability. The following research questions and sub-questions will be used to guide the state-of-the-art review: RQ: How are digital twin technologies currently being developed and applied to support the prevention, early detection, and clinical management of late-life neurodegenerative diseases, and what technical, clinical, and socio-ethical implications emerge from these applications? Sub-questions / Sub-aims A. Technical Feasibility and Innovation RQ: What types of data (e.g., neuroimaging, molecular, behavioral, environmental, and lifestyle data) are integrated into digital twin models for neurodegenerative diseases, and what methodological and computational challenges affect their integration, validation, and scalability? Rationale: Clarifies feasibility, maturity, and innovation at the technical level, while introducing validation and scalability—key criteria in systematic reviews of emerging technologies. B. Clinical Utility and Effectiveness RQ: Through which mechanisms (e.g., risk prediction, disease trajectory modeling, personalization of interventions, in silico simulation) do digital twins aim to support clinical decision-making in late-life neurodegeneration, and what evidence exists regarding their clinical validity, effectiveness, and readiness for implementation? Rationale: Sharpens focus on how digital twins function clinically and on the strength of the current evidence base. C. Socio-Ethical Implications and Desirability RQ: What socio-ethical, legal, and governance issues are associated with the use of digital twins in aging populations and neurodegenerative care, and how do patients, caregivers, clinicians, researchers, and other stakeholders perceive, engage with, and accept these technologies? Rationale: Explicitly distinguishes ethical implications from social acceptance and desirability, aligning well with innovation assessment frameworks (e.g., responsible innovation, ELSI, HTA). The primary outcome of this project will be the identification of two to three potential use cases of digital twin technology in neurodegeneration, which will be developed further with a second phase of funding. By integrating insights from research and lived experiences, this study will lay the groundwork for developing human-centered, value-driven, and inclusive digital twin solutions in dementia care.(10) The ultimate goal is to develop scalable, personalized approaches that empower patients, support caregivers, and enhance clinical decision-making—ensuring that digital health innovations are impactful and equitable.

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