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Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications
Hongyuan Wang, Shiva Toumaj, Arash Heidari, Alireza Souri, Nima Jafari Navimipour, Yiping Jiang. Engineering Applications of Artificial Intelligence, 2025-04-18
Topic: Amyloid, tau and blood-test diagnosis
Neuro Degenerative Disorders (NDDs) involve progressive nerve cell loss, impacting functions like sensation, movement, memory, and cognition, posing life-threatening risks. Despite extensive research, viable therapies remain elusive due to complex pathophysiology. Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), shows promise in NDD diagnosis and treatment by leveraging vast datasets for accurate predictions. However, because AI models are “black boxes,” explainable AI (XAI) had to be created to make sure that physicians and patients would trust and accept it. Early detection is critical to stop degeneration and make things better for patients. Many in-depth studies on XAI are designed explicitly for NDDs. Existing research does not constantly look at how to interpret NDDs, how to evaluate them, or how to keep them safe. This paper fills in these gaps by looking at and grouping XAI methods for different NDDs, to make them easier to understand and use in medical settings. In this paper, we look at the interpretability methods used in various NDD studies. The methods are split into five groups based on the conditions they are used to treat: Frontotemporal Dementia (FTD), Multiple Sclerosis (MS), Amyotrophic Lateral Sclerosis (ALS), and Alzheimer's Disease (AD). It organizes XAI methods into groups and talks about their pros, cons, and clinical importance. The study also finds some important research gaps. For example, it says that there are no good security frameworks and that XAI is hard to use in real-life healthcare settings. By giving helpful information and a plan for future research, this paper shows how XAI could change how NDDs are found, treated, and predicted. AI technologies will be used more in healthcare, and this will help us learn more about these challenging conditions.
Key findings
- It organizes XAI methods into groups and talks about their pros, cons, and clinical importance.
- The study also finds some important research gaps.
- For example, it says that there are no good security frameworks and that XAI is hard to use in real-life healthcare settings.
- By giving helpful information and a plan for future research, this paper shows how XAI could change how NDDs are found, treated, and predicted.
- AI technologies will be used more in healthcare, and this will help us learn more about these challenging conditions.
Why it matters
This paper groups explainable artificial intelligence methods used in studies of neurodegenerative disorders, including Alzheimer's disease, and notes gaps such as the lack of security frameworks and difficulty using these methods in real healthcare settings. It tests no treatment, cure, or prevention.
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Study details
- Evidence strengthLow
How this study scored 42/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Engineering Applications of Artificial Intelligence) |
| Journal's citation record | 2 / 4 | Engineering Applications of Artificial Intelligence: journal h-index 187 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — 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 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 3 / 7 | Cited 30 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 10.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 12.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 2 / 4 | Cited 30 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Yiping Jiang: h-index 19 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Qatar University: institution h-index 377 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 8 institution(s) in 6 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 42 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.engappai.2025.110752
- Original source →
- Free full text: Engineering Applications of Artificial Intelligence →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Wang, H., Toumaj, S., Heidari, A., Souri, A., Navimipour, N. J., & Jiang, Y. (2025). Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2025.110752
Vancouver
Wang H, Toumaj S, Heidari A, Souri A, Navimipour NJ, Jiang Y. Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications. Engineering Applications of Artificial Intelligence. 2025. doi:10.1016/j.engappai.2025.110752.
BibTeX
@article{wang2025-e4e6321c,
author = {Wang, Hongyuan and Toumaj, Shiva and Heidari, Arash and Souri, Alireza and Navimipour, Nima Jafari and Jiang, Yiping},
title = {{Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications}},
journal = {Engineering Applications of Artificial Intelligence},
year = {2025},
doi = {10.1016/j.engappai.2025.110752},
url = {https://doi.org/10.1016/j.engappai.2025.110752}
}
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