Explainable AI in Diagnostic Radiology for Neurological Disorders – a Review

Abstract

Artificial Intelligence (AI) has recently had unprecedented contributions in every walk of life, but it has not been able to work its way into diagnostic medicine and standard clinical practice yet. Although data scientists, researchers, and medical experts have been working in the direction of design and development of Computer Aided Diagnosis (CAD) tools to serve as assistants to doctors, their large-scale adaptation and integration in the healthcare system still seems far-fetched. Diagnostic Radiology is no exception. Imagining techniques like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) scans have been vastly and very effectively employed by radiologists and neurologists for the differential diagnoses of neurological disorders for decades, yet no AI powered systems, to analyze such scans, have been incorporated into the standard operating procedures in healthcare system. Why? It is absolutely understandable that in diagnostic medicine, precious human lives are on the line, and hence there is no room even for the tiniest of mistakes. Nevertheless, with the advent of Explainable Artificial Intelligence (XAI), the old school black boxes of Deep Learning (DL) systems have been unraveled. Would XAI be the turning point for medical experts to finally embrace AI in diagnostic radiology? This review is a humble endeavor to find the answers to these questions. In this review, we present the journey and contributions of AI in developing systems to recognize, preprocess, and analyze brain MRI scans for differential diagnoses of various neurological disorders, with special emphasis on CAD systems embedded with explainability. We also summarize the challenges up ahead that need to be addressed in order to fully exploit the tremendous potential of XAI in its application to medical diagnostics and serve humanity.

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