Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It

• Published in: Diagnostics (Basel) Vol. 15; no. 2; p. 168
• Main Authors: Hafeez, Yasir, Memon, Khuhed, AL-Quraishi, Maged S., Yahya, Norashikin, Elferik, Sami, Ali, Syed Saad Azhar
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2025; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Brain; brain MRI; computer aided diagnosis; CT imaging; Decision making; deep learning; Diagnosis; Diagnostics; Disease; explainable artificial intelligence; Industrialized nations; Medical diagnosis; medical image analysis; Medical imaging equipment; Nervous system diseases; Neurological disorders; Neurology; PET imaging; Physicians; Radiology; Radiology, Medical; Subject specialists; Systematic Review; explainable artificial intelligence; deep learning; computer aided diagnosis; brain MRI; medical image analysis; neurological disorders
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics15020168

Background: Artificial intelligence (AI) has recently made 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 designing and developing computer aided diagnosis (CAD) tools to serve as assistants to doctors, their large-scale adoption and integration into 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 widely 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 of healthcare systems. 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. Methods: 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. A comprehensive review of the literature from 2017 to 2024 was conducted using host databases. We also present medical domain experts’ opinions and 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. Results: Forty-seven studies were summarized and tabulated with information about the XAI technology and datasets employed, along with performance accuracies. The strengths and weaknesses of the studies have also been discussed. In addition, the opinions of seven medical experts from around the world have been presented to guide engineers and data scientists in developing such CAD tools. Conclusions: Current CAD research was observed to be focused on the enhancement of the performance accuracies of the DL regimens, with less attention being paid to the authenticity and usefulness of explanations. A shortage of ground truth data for explainability was also observed. Visual explanation methods were found to dominate; however, they might not be enough, and more thorough and human professor-like explanations would be required to build the trust of healthcare professionals. Special attention to these factors along with the legal, ethical, safety, and security issues can bridge the current gap between XAI and routine clinical practice.