A Overview Of Current Approaches And Applications For Explainable AI In Alzheimer's Detection
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2025
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| author | Ashwini Pujari*, Manasi Rajput, Shruti Patil |
| author_facet | Ashwini Pujari*, Manasi Rajput, Shruti Patil |
| contents | <p><span>A common kind of dementia, Alzheimer’s disease (AD), is characterized via decline in memory and mental decline. Alzheimer’s The, a progressive neurological disease, which poses difficulties in early diagnosis, which is essential to an effective intervention. Traditional AI methods like deep learning and machine learning have demonstrated encouraging outcomes in identifying AD biomarkers and predicting disease progression. However, their “black-box” nature restricts their ability to utility. This review emphasizes how crucial accessibility is to Models of AI, allowing clinicians to understand, trust, and interpret AI-driven predictions. We examine various XAI techniques like based on rules design attention mechanisms, as well as model-agnostic (model independent) approaches, focusing on their role in analyzing neuroimaging data, genetic factors, and cognitive assessments. Additionally, we discuss the XAI’s incorporation into clinical procedures and the potential for personalized medicine in AD care. The review concludes by addressing the difficulties along with possibilities for XAI enhancing the precision, transparency and clinical acceptance of Alzheimer’s Identification systems.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14774029 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Overview Of Current Approaches And Applications For Explainable AI In Alzheimer's Detection Ashwini Pujari*, Manasi Rajput, Shruti Patil Alzheimer's disease, The concept of Artificial Intelligence (AI), Deep learning as well as machine learning. Type of XAI method , Future direction and opportunities. <p><span>A common kind of dementia, Alzheimer’s disease (AD), is characterized via decline in memory and mental decline. Alzheimer’s The, a progressive neurological disease, which poses difficulties in early diagnosis, which is essential to an effective intervention. Traditional AI methods like deep learning and machine learning have demonstrated encouraging outcomes in identifying AD biomarkers and predicting disease progression. However, their “black-box” nature restricts their ability to utility. This review emphasizes how crucial accessibility is to Models of AI, allowing clinicians to understand, trust, and interpret AI-driven predictions. We examine various XAI techniques like based on rules design attention mechanisms, as well as model-agnostic (model independent) approaches, focusing on their role in analyzing neuroimaging data, genetic factors, and cognitive assessments. Additionally, we discuss the XAI’s incorporation into clinical procedures and the potential for personalized medicine in AD care. The review concludes by addressing the difficulties along with possibilities for XAI enhancing the precision, transparency and clinical acceptance of Alzheimer’s Identification systems.</span></p> |
| title | A Overview Of Current Approaches And Applications For Explainable AI In Alzheimer's Detection |
| topic | Alzheimer's disease, The concept of Artificial Intelligence (AI), Deep learning as well as machine learning. Type of XAI method , Future direction and opportunities. |
| url | https://doi.org/10.5281/zenodo.14774029 |