Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detection

Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning (DL) models. The Local Interpretable Model-agnostic Explanations (LIME) and Shaply Additive exPlanation (SHAP) frameworks have grown as popular interpretive tools for ML and DL models. This article provides a systematic review of the application of LIME and SHAP in interpreting the detection of Alzheimer's disease (AD). Adhering to PRISMA and Kitchenham's guidelines, we identified 23 relevant articles and investigated these frameworks' prospective capabilities, benefits, and challenges in depth. The results emphasise XAI's…

• NT
  Nottingham Trent University

  Award: QR Grant 2023
• UR
  UK Research and Innovation

  Award: 10078953
• MO
  Ministry of Higher Education, Research and Innovation

  Award: MoHERI/BFP/UoTAS/01/2021
• EC
  European Commission

  Award: 101095384
• HE
  HORIZON EUROPE Framework Programme

  Award: 101095384