Background and Objectives: Quantum machine learning (QML) leverages quantum computing principles to enhance artificial intelligence applications in health care. We aimed to conduct a systematic literature review following Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 guidelines to evaluate QML and quantum-assisted artificial intelligence (AI) applications in orthopedics and medicine.Materials and Methods: Comprehensive searches were performed across SciSpace, Google Scholar, and PubMed, yielding 1,068 initial records. After deduplication (819 unique records), abstract screening (threshold ≥4.0), and full-text screening (threshold ≥4.5), 97 studies met the inclusion criteria. Screening criteria included medical and orthopedic application, quantum-enhanced AI intervention, comparative performance evaluation, and clinical/computational outcome reporting. Data extraction focused on study design, quantum ML methods, medical application domains, key findings, performance comparisons, and clinical implications. Prediction model Risk of Bias Assessment Tool (PROBAST) guidelines were used to assess risk of the studies that report superior performance of QML over conventional ML or others.Results: The 97 included studies demonstrated diverse applications of QML across multiple medical domains, including orthopedics, oncology, cardiology, neurology, ophthalmology, and other specialties. Quantum approaches included quantum neural networks, quantum support vector machines, variational quantum classifiers, hybrid quantum-classical models, quantum transfer learning, and quantum federated learning. Performance comparisons consistently showed quantum-enhanced methods achieving comparable or superior accuracy to classical approaches. The overall risk of bias for all 11 studies with superior performance was determined as high risk using PROBAST guidelines.Conclusions: QML demonstrates significant promise for enhancing diagnostic accuracy, treatment optimization, and predictive analytics in orthopedics and medicine. Although current implementations face challenges related to hardware limitations, scalability, and clinical validation requirements, the evidence suggests that hybrid quantum-classical approaches offer practical pathways toward clinical adoption with the awareness of reports with high risk of bias. Future studies should focus on large-scale clinical trials and real-world validation of QML.
Background and Objectives: Quantum machine learning (QML) leverages quantum computing principles to enhance artificial intelligence applications in health care. We aimed to conduct a systematic literature review following Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 guidelines to evaluate QML and quantum-assisted artificial intelligence (AI) applications in orthopedics and medicine.Materials and Methods: Comprehensive searches were performed across SciSpace, Google Scholar, and PubMed, yielding 1,068 initial records. After deduplication (819 unique records), abstract screening (threshold ≥4.0), and full-text screening (threshold ≥4.5), 97 studies met the inclusion criteria. Screening criteria included medical and orthopedic application, quantum-enhanced AI intervention, comparative performance evaluation, and clinical/computational outcome reporting. Data extraction focused on study design, quantum ML methods, medical application domains, key findings, performance comparisons, and clinical implications. Prediction model Risk of Bias Assessment Tool (PROBAST) guidelines were used to assess risk of the studies that report superior performance of QML over conventional ML or others.Results: The 97 included studies demonstrated diverse applications of QML across multiple medical domains, including orthopedics, oncology, cardiology, neurology, ophthalmology, and other specialties. Quantum approaches included quantum neural networks, quantum support vector machines, variational quantum classifiers, hybrid quantum-classical models, quantum transfer learning, and quantum federated learning. Performance comparisons consistently showed quantum-enhanced methods achieving comparable or superior accuracy to classical approaches. The overall risk of bias for all 11 studies with superior performance was determined as high risk using PROBAST guidelines.Conclusions: QML demonstrates significant promise for enhancing diagnostic accuracy, treatment optimization, and predictive analytics in orthopedics and medicine. Although current implementations face challenges related to hardware limitations, scalability, and clinical validation requirements, the evidence suggests that hybrid quantum-classical approaches offer practical pathways toward clinical adoption with the awareness of reports with high risk of bias. Future studies should focus on large-scale clinical trials and real-world validation of QML.
Quantum machine learning (QML) has emerged as a promising approach for analyzing the complex, high-dimensional data encountered in oncology, yet research in this area remains fragmented. This systematic literature review synthesizes current applications of QML in cancer care. Following PRISMA guidelines, six databases were searched for peer-reviewed English-language studies published between 2020 and 2026. Of the 212 records identified, 49 studies met the inclusion criteria after screening and quality assessment. The findings show that QML research is dominated by classification and detection tasks, while segmentation is beginning to emerge. Breast cancer and brain tumors are the most frequently investigated domains. Hybrid quantum-classical models, particularly quantum kernel methods, quantum neural networks, and quantum convolutional neural networks, are the predominant approaches. The main barriers to adoption are hardware limitations, including quantum noise, limited qubit availability, and reliance on simulators. Overall, QML in oncology remains in its early stages of development, with limited clinical validation, insufficient model interpretability, and little evidence of a clear quantum advantage. Future research should prioritize evaluation on real quantum hardware, larger and more diverse clinical datasets, standardized benchmarking against classical methods, and closer collaboration between computer scientists and oncology experts to facilitate clinical translation.
Background Artificial Intelligence (AI) incorporation within healthcare applications has significantly helped in the improvement of healthcare diagnostics, patient forecasting, and clinical decision-making. The steady growing of medical data in terms of quantity, quality, and dimension poses more significant problems for traditional AI methods in terms of size, time, and computational efficiency. However, classical machine learning models often struggle with high-dimensional, imbalanced, and heterogeneous medical datasets, which limits their scalability and generalizability. The Quantum Artificial Intelligence (QAI) model, which combines quantum computing and machine learning in a complementary way, has emerged as one of the main trends to defeat these problems. This complementary approach is gaining more popularity, with the most advanced work being developed in this area, as it offers a fundamental explanation of current complexities and the underlying principles and their applications. Methodology This paper begins with the basic concepts of quantum computing and where it overlaps with AI. It then moves to a comparative approach, discussing various quantum machine learning algorithms such as quantum support vector machines, quantum neural networks, and variational quantum circuits. A comparative evaluation of quantum support vector machine, variational quantum classifier, quantum neural network, quantum K-means, and quantum Boltzmann machine is conducted using the Pima Indians Diabetes dataset to assess model behaviour and performance under standard metrics. Further detail is provided on the theoretical and practical implications of QAI in medical imaging, genomics, electronic health records, and drug discovery. Additionally, this paper examines platforms, tools, and quantum hardware used for QAI research, and discusses unique challenges currently faced in quantum technology. Results Unlike prior surveys that primarily provide conceptual overviews, this paper uniquely combines a comprehensive review with practical benchmarking of multiple quantum machine learning models on a benchmark medical dataset. This provides comparative insights into algorithmic performance under real-world constraints, particularly in relation to high-dimensional medical data, genomic data, electronic health records with large number of clinical attributes, radiology images, etc. In this study, dimensionality arises from multivariate clinical features encoded into quantum feature maps and the limitations of current quantum systems. Conclusion This paper aims to serve as a starting point for researchers and practitioners interested in the potential of quantum-enhanced AI to transform health data analytics. By identifying current limitations, evaluating the performance of emerging quantum machine learning approaches, and outlining gaps in the literature, the paper highlights future research directions necessary to advance QAI in healthcare. Overall, the findings outline both the potential and practical constraints of quantum models, offering a foundation for future work toward scalable, clinically applicable QAI solutions.
Quantum Machine Learning (QML) has emerged as a promising interdisciplinary paradigm that integrates quantum computing and machine learning to address limitations of classical approaches in processing complex high-dimensional heterogeneous and imbalanced medical data. This study presents a systematic literature review of QML applications in healthcare based on 94 primary studies published between 2020 and April 2025. The review follows Kitchenham’s guidelines and the PRISMA framework to ensure rigor transparency and reproducibility. Seven research questions were formulated to examine publication trends sources datasets topics quantum model categories algorithms methods and evaluation metrics. Findings reveal a sharp increase in publication activity after 2023 indicating growing academic interest in healthcare-oriented QML. Medical imaging datasets were most frequently used followed by signal-based and tabular datasets with 86.5% of studies relying on public data. Hybrid classical-quantum approaches including CNN-QSVM Quantum Neural Networks and Variational Quantum Classifiers were dominant because they combine classical feature extraction with quantum-enhanced learning. However most studies were conducted in simulation environments rather than real hardware reflecting NISQ limitations including noise decoherence limited qubits and circuit depth constraints. Many results used small curated or imbalanced datasets raising concerns regarding generalizability reproducibility and clinical validity. Although numerous studies reported high accuracy standardized evaluation protocols external validation and uncertainty assessment remain limited. Overall QML in healthcare remains exploratory with substantial potential but significant barriers. This review highlights progress gaps and future directions emphasizing clinically representative datasets robust benchmarking interpretable noise-resilient models and real-hardware implementation to support trustworthy deployment in healthcare systems worldwide settings today.
Classification is a fundamental aspect of leveraging big data for decision-making across domains such as engineering, medicine, economics, and beyond. This systematic review explores the application of quantum machine learning (QML) to address the classification challenges. Despite the rising popularity of QML, challenges persist regarding its practical efficacy and applicability relative to classical methods. This study seeks to classify and analyse the application of QML across diverse domains. Employing a systematic methodology, a total of 23 research articles were chosen after applying rigorous selection criteria between the years 2013 to 2023. At first, 520 research articles were identified, and narrowed down to 296 papers, the abstracts of the papers have been read, 61 full-text papers have been studied, and ultimately 23 papers have been selected for review. A methodological approach was employed, involving the selection of relevant papers from reputable databases, evaluation of these papers, and the subsequent derivation of insights into the performance of QML in practical classification applications. Findings indicate that QML classifiers mostly achieved superior performance compared to conventional methods across accuracy, high-dimensional data management, and computational efficiency. However, challenges remain, including the limitations of noisy quantum hardware and the requirement for hybrid and innovative quantum algorithms. Analysis of current trends reveals significant progress in hybrid quantum-classical computational methods. To conclude, this analysis reveals QML’s revolutionary capabilities while noting its current shortcomings. This study provides a thorough overview for researchers investigating QML’s application to real-world classification problems, and it offers guidelines for future research endeavors.
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.
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.