Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications
<p>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 (A...
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2025
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| _version_ | 1864513534185963520 |
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| author | Hongyuan Wang (348760) |
| author2 | Shiva Toumaj (22467154) Arash Heidari (6845390) Alireza Souri (22467157) Nima Jafari (22467160) Yiping Jiang (11067853) |
| author2_role | author author author author author |
| author_facet | Hongyuan Wang (348760) Shiva Toumaj (22467154) Arash Heidari (6845390) Alireza Souri (22467157) Nima Jafari (22467160) Yiping Jiang (11067853) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hongyuan Wang (348760) Shiva Toumaj (22467154) Arash Heidari (6845390) Alireza Souri (22467157) Nima Jafari (22467160) Yiping Jiang (11067853) |
| dc.date.none.fl_str_mv | 2025-04-18T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.engappai.2025.110752 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Neurodegenerative_disorders_A_Holistic_study_of_the_explainable_artificial_intelligence_applications/30405913 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Neurosciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Neurodegenerative disorders eXplainable artificial intelligence Machine learning Deep learning Early diagnosis |
| dc.title.none.fl_str_mv | Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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.</p><h2>Other Information</h2> <p> Published in: Engineering Applications of Artificial Intelligence<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2025.110752" target="_blank">https://dx.doi.org/10.1016/j.engappai.2025.110752</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_dc2dfe9b70eeb20e7b3dd04969d2b41f |
| identifier_str_mv | 10.1016/j.engappai.2025.110752 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30405913 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applicationsHongyuan Wang (348760)Shiva Toumaj (22467154)Arash Heidari (6845390)Alireza Souri (22467157)Nima Jafari (22467160)Yiping Jiang (11067853)Biomedical and clinical sciencesNeurosciencesHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceNeurodegenerative disorderseXplainable artificial intelligenceMachine learningDeep learningEarly diagnosis<p>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.</p><h2>Other Information</h2> <p> Published in: Engineering Applications of Artificial Intelligence<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2025.110752" target="_blank">https://dx.doi.org/10.1016/j.engappai.2025.110752</a></p>2025-04-18T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.engappai.2025.110752https://figshare.com/articles/journal_contribution/Neurodegenerative_disorders_A_Holistic_study_of_the_explainable_artificial_intelligence_applications/30405913CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304059132025-04-18T12:00:00Z |
| spellingShingle | Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications Hongyuan Wang (348760) Biomedical and clinical sciences Neurosciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Neurodegenerative disorders eXplainable artificial intelligence Machine learning Deep learning Early diagnosis |
| status_str | publishedVersion |
| title | Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications |
| title_full | Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications |
| title_fullStr | Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications |
| title_full_unstemmed | Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications |
| title_short | Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications |
| title_sort | Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications |
| topic | Biomedical and clinical sciences Neurosciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Neurodegenerative disorders eXplainable artificial intelligence Machine learning Deep learning Early diagnosis |