Multimodal approaches and AI-driven innovations in dementia diagnosis: a systematic review
Abstract Neurodegenerative disorders, such as dementia, present some of the most pressing challenges in the field of medicine today. By causing progressive cognitive and functional decline, Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) subtypes are an essential area for urgently needed...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-06-01
|
| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-025-00358-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849691323451310080 |
|---|---|
| author | Revati M. Wahul Sarita Ambadekar Deepesh M. Dhanvijay Mrinai M. Dhanvijay Manisha A. Dudhedia Varsha Gaikwad Bhavana Kanawade J. R. Pansare Balaji Bodkhe S. H. Gawande |
| author_facet | Revati M. Wahul Sarita Ambadekar Deepesh M. Dhanvijay Mrinai M. Dhanvijay Manisha A. Dudhedia Varsha Gaikwad Bhavana Kanawade J. R. Pansare Balaji Bodkhe S. H. Gawande |
| author_sort | Revati M. Wahul |
| collection | DOAJ |
| description | Abstract Neurodegenerative disorders, such as dementia, present some of the most pressing challenges in the field of medicine today. By causing progressive cognitive and functional decline, Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) subtypes are an essential area for urgently needed work. As a systematic literature review, this paper outlines the intricacies of dementia’s pathophysiology of dementia by addressing the complexity of dementia as a construct, how different types of clinical paths can happen, how neuronal atrophy occurs along different cerebral domains, and when the critical diagnostic thresholds are met. A complete review of peer-reviewed papers over the past ten years, with a focus on Machine Learning, Deep Learning, and multimodal fusion approaches to enhance diagnostic and therapeutic precision will focus on neuroimaging biomarkers, EEG-based cognitive profiles, digital phenotyping, and wearable sensor analytics. This survey will compare the study’s algorithms or frameworks on sensitivity, specificity, interpretability, computational efficiency, and clinical transnationality concerning early detection and monitoring progression. Though AI methods are having a continuing rapid surge in progress, the issues of model transparency and generalizability are still lacking, thus meaning the need for XAI. This work builds a multi-disciplinary data agnostic approach for building stronger patient-centered models that can bring together genomics, imaging, behavior and contextual features in the task-driven processes. Overall, this literature survey’s objective is to shine a light on the multi-faceted pathway towards precision-driven, AI augmented dementia care—and ultimately to change the management of neurodegenerative disease by synthesizing current developments and highlighting their shortcomings. |
| format | Article |
| id | doaj-art-49fcf894e2b64f40b0602f0c1de07f2d |
| institution | DOAJ |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-49fcf894e2b64f40b0602f0c1de07f2d2025-08-20T03:21:03ZengSpringerDiscover Artificial Intelligence2731-08092025-06-015113210.1007/s44163-025-00358-xMultimodal approaches and AI-driven innovations in dementia diagnosis: a systematic reviewRevati M. Wahul0Sarita Ambadekar1Deepesh M. Dhanvijay2Mrinai M. Dhanvijay3Manisha A. Dudhedia4Varsha Gaikwad5Bhavana Kanawade6J. R. Pansare7Balaji Bodkhe8S. H. Gawande9Department of Computer Engineering, Wadia College of Engineering, S. P. Pune UniversityDepartment of Computer Engineering, K. J. Somaiya Institute of TechnologyDepartment of Electronics and Communication Engineering, National Institute of Technology, TrichyDepartment of Electronics and Telecommunication Engineering, Wadia College of EngineeringDepartment of Electronics and Telecommunication, Marathwada Mitra Mandal’s College of EngineeringDepartment Information Technology, Government College of EngineeringDepartment of Information Technology, International Institute of Information Technology, S. P. Pune UniversityDepartment of Computer Engineering, Wadia College of Engineering, S. P. Pune UniversityDepartment of Computer Engineering, Wadia College of Engineering, S. P. Pune UniversityIndustrial Tribology Laboratory, Department of Mechanical Engineering, Wadia College of Engineering, S. P. Pune UniversityAbstract Neurodegenerative disorders, such as dementia, present some of the most pressing challenges in the field of medicine today. By causing progressive cognitive and functional decline, Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) subtypes are an essential area for urgently needed work. As a systematic literature review, this paper outlines the intricacies of dementia’s pathophysiology of dementia by addressing the complexity of dementia as a construct, how different types of clinical paths can happen, how neuronal atrophy occurs along different cerebral domains, and when the critical diagnostic thresholds are met. A complete review of peer-reviewed papers over the past ten years, with a focus on Machine Learning, Deep Learning, and multimodal fusion approaches to enhance diagnostic and therapeutic precision will focus on neuroimaging biomarkers, EEG-based cognitive profiles, digital phenotyping, and wearable sensor analytics. This survey will compare the study’s algorithms or frameworks on sensitivity, specificity, interpretability, computational efficiency, and clinical transnationality concerning early detection and monitoring progression. Though AI methods are having a continuing rapid surge in progress, the issues of model transparency and generalizability are still lacking, thus meaning the need for XAI. This work builds a multi-disciplinary data agnostic approach for building stronger patient-centered models that can bring together genomics, imaging, behavior and contextual features in the task-driven processes. Overall, this literature survey’s objective is to shine a light on the multi-faceted pathway towards precision-driven, AI augmented dementia care—and ultimately to change the management of neurodegenerative disease by synthesizing current developments and highlighting their shortcomings.https://doi.org/10.1007/s44163-025-00358-xDementia diagnosisAlzheimer’s disease (AD)Frontotemporal dementia (FTD)Machine learning (ML)Deep learning (DL)Multimodal data Integration |
| spellingShingle | Revati M. Wahul Sarita Ambadekar Deepesh M. Dhanvijay Mrinai M. Dhanvijay Manisha A. Dudhedia Varsha Gaikwad Bhavana Kanawade J. R. Pansare Balaji Bodkhe S. H. Gawande Multimodal approaches and AI-driven innovations in dementia diagnosis: a systematic review Discover Artificial Intelligence Dementia diagnosis Alzheimer’s disease (AD) Frontotemporal dementia (FTD) Machine learning (ML) Deep learning (DL) Multimodal data Integration |
| title | Multimodal approaches and AI-driven innovations in dementia diagnosis: a systematic review |
| title_full | Multimodal approaches and AI-driven innovations in dementia diagnosis: a systematic review |
| title_fullStr | Multimodal approaches and AI-driven innovations in dementia diagnosis: a systematic review |
| title_full_unstemmed | Multimodal approaches and AI-driven innovations in dementia diagnosis: a systematic review |
| title_short | Multimodal approaches and AI-driven innovations in dementia diagnosis: a systematic review |
| title_sort | multimodal approaches and ai driven innovations in dementia diagnosis a systematic review |
| topic | Dementia diagnosis Alzheimer’s disease (AD) Frontotemporal dementia (FTD) Machine learning (ML) Deep learning (DL) Multimodal data Integration |
| url | https://doi.org/10.1007/s44163-025-00358-x |
| work_keys_str_mv | AT revatimwahul multimodalapproachesandaidriveninnovationsindementiadiagnosisasystematicreview AT saritaambadekar multimodalapproachesandaidriveninnovationsindementiadiagnosisasystematicreview AT deepeshmdhanvijay multimodalapproachesandaidriveninnovationsindementiadiagnosisasystematicreview AT mrinaimdhanvijay multimodalapproachesandaidriveninnovationsindementiadiagnosisasystematicreview AT manishaadudhedia multimodalapproachesandaidriveninnovationsindementiadiagnosisasystematicreview AT varshagaikwad multimodalapproachesandaidriveninnovationsindementiadiagnosisasystematicreview AT bhavanakanawade multimodalapproachesandaidriveninnovationsindementiadiagnosisasystematicreview AT jrpansare multimodalapproachesandaidriveninnovationsindementiadiagnosisasystematicreview AT balajibodkhe multimodalapproachesandaidriveninnovationsindementiadiagnosisasystematicreview AT shgawande multimodalapproachesandaidriveninnovationsindementiadiagnosisasystematicreview |