Enhancing Brain Disease Diagnosis with XAI: A Review of Recent Studies

Abstract

The area of eXplainable Artificial Intelligence (XAI) has shown remarkable progress in the past few years, with the aim of enhancing the transparency and interpretability of the Machine Learning (ML) and Deep Learning (DL) models. This review article presents an in-depth review of the current state-of-the-art XAI techniques applied to the diagnosis of brain diseases. The challenges encountered by traditional ML and DL models within this domain are thoroughly examined, emphasizing the pivotal role of XAI in providing the transparency and interpretability of these models. Furthermore, this article presents a comprehensive survey of the XAI methodologies used for making diagnoses of various brain disorders. Recent studies utilizing XAI for diagnosing a range of brain illnesses, including Alzheimer, brain tumors, dementia, Parkinson, multiple sclerosis, autism, epilepsy, and stroke, are critically reviewed. Finally, the limitations inherent in current XAI techniques are discussed, along with prospective avenues for future research. The key goal of this study is to provide researchers with a roadmap that shows the potential of XAI techniques in improving the interpretability and transparency of DL and ML algorithms for the diagnosis of brain diseases, while also delineating the challenges that require concerted research efforts.

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