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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.
Wang et al. (Fri,) studied this question.