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Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection
Vimbi V, Shaffi N, Mahmud M.. Brain informatics, 2024-04-05
Topic: Brain imaging, connectivity and assessment
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.
Key findings
- The results emphasise XAI's crucial role in strengthening the trustworthiness of AI-based AD predictions.
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Study details
- Study designsystematic review
- Evidence strengthHigh
How this study scored 72/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size, effect reported and studied in people because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 42 of the 58 points we could judge it on, which is 72 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Brain informatics) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 12 / 12 | A systematic review of all the studies on the question |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | A review of other studies — randomisation is not credited here |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38578524) |
| Free to read | 2 / 2 | Free to read |
| Points | 42 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 42 of the 58 points we could judge it on is 72 out of 100. |
| Total | 72 / 100 | — |
What each of these 20 components measures, and what this score is not →
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APA 7th
Vimbi, V., Shaffi, N., & Mahmud, M. (2024). Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection. Brain informatics. https://doi.org/10.1186/s40708-024-00222-1
Vancouver
Vimbi V, Shaffi N, Mahmud M. Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection. Brain informatics. 2024. doi:10.1186/s40708-024-00222-1. PMID: 38578524.
BibTeX
@article{vimbi2024-38578524,
author = {Vimbi, V. and Shaffi, N. and Mahmud, M.},
title = {{Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection}},
journal = {Brain informatics},
year = {2024},
doi = {10.1186/s40708-024-00222-1},
pmid = {38578524},
url = {https://doi.org/10.1186/s40708-024-00222-1}
}