ReviewBrain informatics2024
Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection.
Review in Brain informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
105 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Explainability, Bias and Generalizability of AI Models in Dentistry: A Systematic Review of Model Interpretability and Equity.Clinical and experimental dental research · 2026Pooled it
- Sustainable smart sensing and AI-driven platforms for real-time detection and monitoring of mycotoxins across the food supply chain.Mycotoxin research · 2026Pooled it
- Artificial intelligence in breast cancer research: a systematic review and bibliometric analysis of emerging trends and future directions.Frontiers in oncology · 2026Pooled it
- A systematic review of EEG-based machine learning classifications for obsessive-compulsive disorder: current status and future directions.BMC psychiatry · 2025Pooled it
- The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.BMC medical informatics and decision making · 2025Pooled it
- Interpretable Machine Learning for In-Hospital Mortality Prediction in ICU Patients Using First-24-Hour Routine Vital Signs: A SHAP-Based MIMIC-IV Study.Diagnostics (Basel, Switzerland) · 2026Article
- Neurofilament Light Chain (NfL) in Neurodegenerative Diseases: Biological and Clinical Significance, Multi-Omics Integration, and AI-Driven Biomarker Modeling for Precision Therapy.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Theranostic Smart Bionanosensors for Solid Tumors: Advances, Challenges, and Future Directions.Applied biochemistry and biotechnology · 2026Review
- Development and Validation of an XGBoost-SHAP Model for Predicting Adverse Outcomes in Elderly Cardiovascular Patients With Polypharmacy: A Retrospective Cohort Study.Pharmacology research & perspectives · 2026Article
- Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine.International journal of molecular sciences · 2026Review
- RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.Brain informatics · 2026Review
- Raman spectroscopy combined with multiple technologies for label-free identification of immune cells: An overview.Journal of pharmaceutical analysis · 2026Review
- Differential quadruple pattern: A new EEG signal classification framework.IBRO neuroscience reports · 2026Article
- Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer, Dictionary Version PM1.0.Journal of imaging informatics in medicine · 2026Article
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Explainable machine learning for the prediction of Alzheimer's disease-related cognitive impairment: a consensus feature selection approach.BMC medical informatics and decision making · 2026Article
- Immunological Drug-Drug Interactions in Immune Checkpoint Inhibitor Therapy: Mechanisms, Clinical Evidence, and Artificial Intelligence.Current oncology reports · 2026Review
- Integrating Metabolomics and Machine Learning for Advanced Chemical Detection.Sensors (Basel, Switzerland) · 2026Review
- The Role of AI in Clinical Trial Design and Scientific Writing.Cardiovascular and interventional radiology · 2026Review
- Machine learning based retrospective cohort study to predict mortality risk in Chinese patients undergoing extracorporeal membrane oxygenation.Scientific reports · 2026Article
45 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
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 crucial role in strengthening the trustworthiness of AI-based AD predictions. This review aims to provide fundamental capabilities of LIME and SHAP XAI frameworks in enhancing fidelity within clinical decision support systems for AD prognosis.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.