Dynamic Explainability in AI for Neurological Disorders: An Adaptive Model for Transparent Decision-Making in Alzheimer's Disease Diagnosis

In this paper, we proposed a model that will solve the ‘X’ of the ‘Xai’ that is Explainable AI. The model is developed using deep learning and transfer learning algorithms using different methods to depict how the decisions and predictions of the Artificial Intelligence are made to be understandable for humans to interpret. The term deals with explaining how the models work and what all happens in each layer of neurons and the output is shown. The transparency in the process lets humans understand the way how the predictions are carried out, what are the parameters that the model is considering, what are the steps it takes to generate the final output. Here, we considered Alzheimer disease in the brain and brought out the results per layer of the model to comprehend the reason of the final result. This could made easy for humans to identify what are the errors, unknown biases and the number of possible paths the model can take in order to generate the more accurate output. This proposed model is able to identify and depict the processes going on while generating the result. The field tends to address the “black box” problem in the complex machine learning models. The analysis would be used by stakeholders to identify the real cause behind the interpretation of results. Considering these, there are many use cases for this new emerging field, some of them includes in the field of medical diagnosis, where the doctors would be able to identify the paradigm and produce the most accurate diagnosis that will cure the patient's disease, in the finance sector to identify the future trends, for the real time processing and many such fields.