Abstract Quantum computing (QC) has emerged as a transformative technology with the potential to surpass classical computational limits, especially in complex domains like medical imaging. This study investigates the integration of QC and quantum machine learning (QML) into medical imaging, with a focus on MRI, EEG, and CT. A structured literature review was conducted to identify recent developments, focusing on studies using accessible open-source data and English-language peer-reviewed publications. By leveraging quantum principles such as superposition and entanglement, hybrid quantum-classical models have demonstrated enhanced accuracy, speed, and efficiency in diagnostic tasks. In MRI, QML has improved early detection and classification of neurological diseases like Alzheimer’s and brain tumours. In EEG analysis, quantum algorithms such as Quantum EEGNet and Quantum Support Vector Machines have shown superior performance in detecting disorders, including schizophrenia and autism spectrum disorder. Additionally, quantum algorithms for CT image reconstruction and classification have achieved faster processing with fewer artifacts and greater fidelity. Despite current hardware constraints, results highlight the promise of QC in addressing existing limitations in medical diagnostics. The findings support continued development of QML models and quantum- enhanced workflows, with potential to revolutionise clinical practice by offering more accurate, efficient, and personalised care in neurological imaging and analysis.