Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detection
Brain Informatics, ISSN: 2198-4026, Vol: 11, Issue: 1, Page: 10
2024
- 25Citations
- 146Captures
- 2Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations25
- Citation Indexes25
- 25
- Captures146
- Readers146
- 146
- Mentions2
- News Mentions2
- 2
Most Recent News
Research from University of Technology and Applied Sciences in the Area of Artificial Intelligence Described (Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection)
2024 APR 23 (NewsRx) -- By a News Reporter-Staff News Editor at Pain & Central Nervous System Daily News -- Researchers detail new data in
Review Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85189744471&origin=inward; http://dx.doi.org/10.1186/s40708-024-00222-1; http://www.ncbi.nlm.nih.gov/pubmed/38578524; https://braininformatics.springeropen.com/articles/10.1186/s40708-024-00222-1; https://dx.doi.org/10.1186/s40708-024-00222-1
Springer Science and Business Media LLC
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