Background: Artificial intelligence (AI) is increasingly investigated for radiographic assessment of peri-implant bone conditions, although radiographic marginal bone loss does not alone establish a clinical diagnosis of peri-implantitis. This narrative review aimed to critically synthesize current evidence on AI-assisted radiographic assessment of peri-implant marginal bone levels and bone loss. Methods: A structured literature search was conducted in PubMed/MEDLINE and Scopus. Eligible original studies applying AI or machine-learning methods to human dental radiographs for peri-implant bone assessment were included in a qualitative synthesis. Results: Seventeen studies were included. Periapical radiographs were the predominant imaging modality, while panoramic radiographs and CBCT were less frequently investigated. AI applications included implant and landmark localization, marginal bone-level measurement and quantification, bone-loss severity and defect-morphology classification, and prediction using imaging-derived features. Reported performance was generally promising, although substantial heterogeneity was observed in datasets, target tasks, reference standards, validation strategies, and performance metrics. Conclusions: Current radiograph-based AI systems show potential for image analysis, quantitative assessment, and clinical decision support. However, radiographic AI performance should not be interpreted as autonomous diagnostic accuracy for peri-implantitis. External validation, longitudinal assessment, and integration with relevant clinical parameters remain priorities for future research.
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.
