Quantum Machine Learning (QML) is an interdisciplinary field combining Quantum Computing (QC) and Machine Learning (ML). It has gained increased attention due to advances in near-term hardware implementations of quantum devices. The use of QML has proven to result in a significant improvement in performance and computational speed. Consequently, QML has become an effective technique for data processing and classification. Researchers have recently proposed various QML solutions in the medical image analysis field to gain an advantage of quantum supremacy. The main objective of this work is to present a holistic review of current leading-edge published works in the quantum medical image analysis field with a focus on supervised learning using artificial neural networks. A comparative study is used to pinpoint the potential of existing techniques, the most promising techniques, and the future of research in this area.
Quantum Machine Learning (QML) is an interdisciplinary field combining Quantum Computing (QC) and Machine Learning (ML). It has gained increased attention due to advances in near-term hardware implementations of quantum devices. The use of QML has proven to result in a significant improvement in performance and computational speed. Consequently, QML has become an effective technique for data processing and classification. Researchers have recently proposed various QML solutions in the medical image analysis field to gain an advantage of quantum supremacy. The main objective of this work is to present a holistic review of current leading-edge published works in the quantum medical image analysis field with a focus on supervised learning using artificial neural networks. A comparative study is used to pinpoint the potential of existing techniques, the most promising techniques, and the future of research in this area.
The combination of Quantum Computing with Machine Learning approaches is refered as Quantum Machine Learning (QML). Recently, there have been significant advancements in performance and computational speed to quantum computing techniques. Although traditional algorithms have shown promising outcomes, their low processing efficiency and limited labelled data are common problems. To address these issues, a great deal of research has brought machine learning and quantum computing together, which has led to the development of increasingly complex algorithms. Notable gains in parameter optimisation, execution efficiency, and error rate reduction have resulted from these developments. The incorporation of quantum machine learning has yielded novel perspectives into the transdisciplinary domain of quantum technology and medical image analysis, indicating noteworthy advancements to be made in this area going forward. QML is becoming known for its unique superposition and entanglement capabilities, which make it a cutting-edge approach to challenges involving categorization. Its uses in fields such as pattern identification, password cracking, and medical image analysis have attracted a lot of attention. Because of this, a growing number of academics are using QML-based algorithms to analyse medical data. This research review's key goal is to present an in-depth analysis of how QML techniques are used and involved in the medical field. This article includes a synopsis of the description and classification of quantum machine learning in addition to a rundown of the many QML techniques and how they have been used in medical image investigation during the previous years.
Quantum Machine Learning (QML) is an emerging, powerful paradigm at the intersection of quantum computing and artificial intelligence, with the potential to enhance medical image analysis. We compared their performance with classical counterparts, while critically examining the gap between theoretical quantum advantages and the practical constraints of current Noisy Intermediate-Scale Quantum (NISQ) implementations. This review explored the current landscape of QML-based methodologies applied to key medical imaging modalities - Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-rays, and histopathology. We systematically analyzed how quantum-enhanced models were employed for critical tasks such as image classification, segmentation, and reconstruction, and we compared their performance with that of classical counterparts. The review delved into the architectural innovations that enabled these quantum approaches, including hybrid quantum-classical frameworks, variational quantum circuits, and quantum convolutional neural networks. Particular emphasis was given to the clinical relevance of QML applications, from early diagnostics to real-time surgery planning, and how quantum advantage might influence data efficiency, interpretability, along computational scalability. Finally, we highlighted current challenges in data encoding, noise resilience, and hardware limitations, and discussed promising future directions that could enable the practical deployment of QML in healthcare settings. This comprehensive review aimed to serve as a foundational reference for researchers and practitioners looking to harness quantum computing in medical imaging and precision medicine.
Quantum technologies are rapidly advancing as image classification tasks grow more complex due to large image volumes and extensive parameter updates required by traditional machine learning models. Quantum Machine Learning (QML) offers a promising solution for medical image classification. The parallelization of quantum computing can significantly improve speed and accuracy in disease detection and diagnosis. This paper provides an overview of recent studies on medical image classification through a structured taxonomy, highlighting key contributions, limitations and gaps in current research. It emphasizes moving from simulations to real quantum computers, addressing challenges like noisy qubits and suggests future research to enhance medical image classification using quantum technology.
Quantum Learning (QL) has emerged as a promising approach to medical image classification, leveraging the principles of quantum mechanics to improve the performance and efficiency of machine learning algorithms. This systematic review provides a comprehensive critical assessment of the current status of QL techniques developed for medical image classification, with a specific focus on trends, methodologies, and future directions in this rapidly evolving field. A thorough literature search was conducted across five major databases, resulting in a total of 28 relevant studies published between 2018 and 2024. The studies were analyzed and classified based on the type of quantum algorithm, the medical image modality, and the performance metrics used. The analysis revealed a diverse range of QL techniques, including Quantum Support Vector Machines (QSVM), Quantum Convolutional Neural Networks (QCNN), and various hybrid quantum-classical approaches. These techniques have been applied to diverse medical image classification tasks, such as brain tumor classification, skin lesion classification, and COVID-19 detection, demonstrating promising results in terms of accuracy, sensitivity, and specificity. However, several challenges were identified, including the preprocessing and encoding of medical images for quantum processing, the limited scalability of current quantum hardware, and the need for interpretable and explainable QL models. This review underscores the immense potential of QL to revolutionize medical image classification, while also emphasizing the necessity of multidisciplinary collaborations and further research to overcome existing challenges and facilitate the integration of QL techniques into clinical practice. Reason for Expression of Concern:The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in ``Quantum Machine and Deep Learning for Medical Image Classification: A Systematic Review of Trends, Methodologies, and Future Directions (Manuscript 1252)''. The journal has initiated an additional editorial assessment of the article's methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal's policies and COPE guidance." See expression of concern available at: DOI: https://doi.org/10.52866/2788-7421.1387. Available at: http://ijcsm.researchcommons.org/ijcsm/vol7/iss1/41 We have found no evidence of misconduct or invalidity in the research. The gindings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research. See resolution of expression of concern available at: DOI: https://doi.org/10.52866/2788-7421.1408 Available at: http://ijcsm.researchcommons.org/ijcsm/vol7/iss2/8 The publication timeline is as follows: Original Article → Expression of Concern → Resolution.
Abstract The convergence of quantum computing and medical image analysis has emerged as a promising frontier for advancing diagnostic intelligence beyond the limits of classical machine learning. Recent developments in quantum hardware and quantum machine learning (QML) algorithms have stimulated growing interest in their potential to enhance complex medical imaging tasks, including disease classification, image segmentation, and clinical decision support (Wei et al., 2023; Ullah and Garcia-Zapirain, 2024). This study presents a comprehensive review and synthesis of state-of-the-art QML methodologies applied to medical imaging, systematically examining both theoretical foundations and empirical implementations. We analyze quantum-based and hybrid quantum–classical models employed across modalities such as MRI, CT, X-ray, and histopathological imaging, with particular attention to classification accuracy, computational efficiency, and diagnostic robustness (Maheshwari et al., 2022; ElBedoui et al., 2025). Our findings reveal clear trends indicating that hybrid quantum–classical approaches currently dominate practical applications, consistently outperforming purely classical baselines in select low-dimensional and domain-specific diagnostic tasks. Performance gains are most evident in feature-rich yet data-constrained scenarios, where quantum kernels and variational circuits demonstrate enhanced representational capacity (Ajlouni et al., 2023; Landman et al., 2022). However, these advantages remain context-dependent and sensitive to noise, circuit depth, and encoding strategies. Overall, QML holds significant promise for improving medical image-based diagnosis, but its widespread clinical adoption is constrained by hardware scalability, limited qubit coherence, and unresolved data encoding challenges. Addressing these limitations will be critical for translating QML from experimental feasibility to routine medical practice (Marengo and Santamato, 2025; Rawas and AlSaeed, 2026).
Quantum machine learning (QML) is an emerging field combining quantum computing and artificial intelligence, with promising applications in medicine and healthcare. This survey reviews more than 60 studies published between 2018 and 2025, highlighting a sharp increase in research activity, especially in the last three years. We address seven core research questions related to publication trends, the use of real quantum hardware versus simulators, quantum architectures overview, dataset types, medical domains, algorithmic frameworks, and reported results. Our analysis shows that most QML research in healthcare is conducted on simulators due to limited hardware access, and it relies on small datasets. Quantum convolutional neural network (QCNN) architectures dominate image-based medical tasks such as tumor detection, pneumonia diagnosis, and ECG interpretation, while feature-based datasets are mainly analyzed with variational quantum classifiers and quantum support vector machines. Despite hardware constraints, QML models often match or surpass classical machine learning approaches in accuracy, frequently reaching 95–99%. However, these performance statements should be qualified to recognize experimental limitations and avoid excessive optimism and should not be interpreted as definitive proof of quantum superiority at this stage. Additionally, issues with reproducibility and reporting of hardware details persist, which is a significant research gap. This review emphasizes the need for standardized benchmarks, more real hardware testing, and architecture-aware algorithm design. With the potential for accelerated diagnostics and personalized healthcare, QML represents a strategic direction for future medical research.