The Rise of Quantum Intelligence in Multiple Sclerosis Diagnosis: A Scopus-Based Bibliometric Roadmap

Multiple Sclerosis (MS) diagnosis, heavily reliant on Magnetic Resonance Imaging (MRI), presents significant challenges that Artificial Intelligence (AI) aims to overcome. Quantum-Inspired Neural Networks (QINNs) and other advanced machine learning models offer promising avenues for enhancing diagnostic accuracy and efficiency. However, the field is nascent and lacks a systematic overview of its intellectual structure and trajectory. This study presents a comprehensive bibliometric analysis to map the research landscape of machine learning applications in MRI-based MS diagnosis from 2006 to 2024. Utilizing a refined dataset of 560 publications from the Scopus database, we employed performance analysis, co-authorship mapping, and temporal keyword analysis using VOSviewer and ScientoPy. The findings reveal a field characterized by exponential growth, particularly since 2018, with a clear technological shift from classical machine learning to more sophisticated deep learning and AI models. Our analysis identifies key international research collaborations, with the USA as a leading contributor and India as a rapidly emerging hub. Furthermore, the keyword analysis highlights a thematic evolution towards critical research hotspots such as "biomarkers," "deep learning," and "artificial intelligence," indicating the field's future directions. This paper provides a data-driven roadmap of the field's evolution, offering valuable insights for researchers and clinicians to strategically navigate and contribute to the next generation of computational diagnostics in MS care.