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Translational computerized clinical decision support systems for Alzheimer's disease: A systematic review

  • Pinya Lu
  • , Mingfeng Chen
  • , Lili Chen
  • , Fan Lin
  • , Hongqin Yang
  • , Yuhua Wang
  • , Xuemei Ding

Research output: Contribution to journalReview articlepeer-review

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Abstract

Background Alzheimer's disease (AD), marked by progressive memory loss and cognitive decline, poses diagnostic challenges due to its multifactorial nature. Therefore, researchers are increasingly leveraging artificial intelligence and data-driven approaches to develop computerized clinical decision support systems (CCDSS), aiming to enhance early detection, improve treatment, and slow disease progression. Objective This study seeks to conduct a systematic review of the most recently developed AD-CCDSS, delving into their progress and the challenges to guide future development and implementation of CCDSS for AD-related decision-making and intervention strategies. Methods We follow the PRISMA 2020 guideline to search for articles published within the past seven years across PubMed, ScienceDirect, IEEE Xplore Digital Library, Web of Science, and Scopus, with Google Scholar as a supplementary source. Key components are then extracted from the selected studies for qualitative analysis, including data modalities, computational modeling approaches, system explainability and interpretability, research priorities, and graphical user interfaces designed for non-technical stakeholders. Results After searching and removing duplicates, we meticulously selected 55 studies. After reviewing key components of CCDSS, we highlight advancements and potential clinical applications, demonstrating their promise in enhancing decision support. However, despite growing attention to explainability in AD-CCDSS, its clinical applicability remains limited. Moreover, challenges such as multi-center system interoperability and data security remain underexplored, hindering real-world implementation. Conclusions This study analyzes recent translational AD-CCDSS, identifying key challenges in advancing CCDSS for clinical applications. It offers insights for researchers to enhance CCDSS development and facilitate their integration into clinical practice.
Original languageEnglish
Pages (from-to)443-483
Number of pages41
JournalJournal of Alzheimer's Disease
Volume106
Issue number2
Early online date1 Jul 2025
DOIs
Publication statusPublished (in print/issue) - 31 Jul 2025

Bibliographical note

© The Author(s) 2025

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was support by the Research Development Projects of Fujian Normal University, China (grant numbers DH-1736, DH-1711); Joint Funds for the Innovation of Science and Technology, Fujian province, China (grant number 2023Y9283); ARUK NI networking grant (grant number 71573R).

FundersFunder number
Fujian Normal UniversityDH-1736, DH-1711
2023Y9283
71573R

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    Keywords

    • Alzheimer’s disease
    • artificial intelligence
    • clinical decision support system
    • clinical translation
    • dementia
    • mild cognitive impairment
    • Humans
    • Artificial Intelligence
    • Alzheimer Disease/therapy
    • Decision Support Systems, Clinical
    • Translational Research, Biomedical
    • Alzheimer's disease

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