Quantum Neural Networks in Magnetic Resonance Imaging: Advancing Diagnostic Precision Through Emerging Computational Paradigms

Background: Quantum Neural Networks (QNNs) combine quantum computing and artificial intelligence to provide powerful solutions for high-dimensional data analysis. In magnetic resonance imaging (MRI), they address the challenges of advanced imaging sequences and data complexity, enabling faster optimization, enhanced feature extraction, and real-time clinical applications. Methods: A literature review using Scopus, PubMed, IEEE Xplore, ACM Digital Library and arXiv identified 84 studies on QNNs in MRI. After filtering for peer-reviewed original research, 20 studies were analyzed. Key parameters such as datasets, architectures, hardware, tasks, and performance metrics were summarized to highlight trends and gaps. Results: The analysis identified datasets supporting tasks like tumor classification, segmentation, and disease prediction. Architectures included hybrid models (e.g., ResNet34 with quantum circuits) and novel approaches (e.g., Quantum Chebyshev Polynomials). Hardware ranged from high-performance GPUs to quantum-specific devices. Performance varied, with accuracy up to 99.5% in some configurations but lower results for complex or limited datasets. Conclusions: The findings provide the first glimpse into the potential of QNNs in MRI, demonstrating accuracy and specificity in diagnostic tasks and biomarker detection. However, challenges such as dataset variability, limited quantum hardware access, and reliance on simulators remain. Future research should focus on scalable quantum hardware, standardized datasets, and optimized architectures to support clinical applications and precision medicine.