The growth in digitization of health data provides opportunities for using algorithmic techniques for data analysis. This systematic review assesses whether quantum machine learning (QML) algorithms outperform existing classical methods for clinical decisioning or health service delivery. Included studies use electronic health/medical records, or reasonable proxy data, and QML algorithms designed for quantum computing hardware. Databases PubMed, Embase, IEEE, Scopus, and preprint server arXiv were searched for studies dated 01/01/2015-10/06/2024. Of an initial 4915 studies, 169 were eligible, with 123 then excluded for insufficient rigor. Only 16 studies consider realistic operating conditions involving quantum hardware or noisy simulations. We find nearly all encountered quantum models form a subset of general QML structures. Scalability of data encoding is partly addressed but requires restrictive hardware assumptions. Overall, performance differentials between quantum and classical algorithms show no consistent trend to support empirical quantum utility in digital health.
The growth in digitization of health data provides opportunities for using algorithmic techniques for data analysis. This systematic review assesses whether quantum machine learning (QML) algorithms outperform existing classical methods for clinical decisioning or health service delivery. Included studies use electronic health/medical records, or reasonable proxy data, and QML algorithms designed for quantum computing hardware. Databases PubMed, Embase, IEEE, Scopus, and preprint server arXiv were searched for studies dated 01/01/2015-10/06/2024. Of an initial 4915 studies, 169 were eligible, with 123 then excluded for insufficient rigor. Only 16 studies consider realistic operating conditions involving quantum hardware or noisy simulations. We find nearly all encountered quantum models form a subset of general QML structures. Scalability of data encoding is partly addressed but requires restrictive hardware assumptions. Overall, performance differentials between quantum and classical algorithms show no consistent trend to support empirical quantum utility in digital health.
The growing digitalization of medical diagnostics has resulted in massive amounts of complicated data, including medical imaging and genomic sequences, clinical writing and real-time patient monitoring. Classical machine learning (CML) has achieved amazing success in evaluating such data. But its computational restrictions hinder scalability and efficiency when dealing with high-dimensional biomedical problems. Quantum machine learning (QML) combines the principles of quantum computing (QC) with advanced learning algorithms to offer a transformative paradigm for digital healthcare. This paper provides a systematic overview of QML foundations including quantum data encoding (QDC), variational quantum circuits (VQC), kernel methods, and hybrid quantum classical models. This paper also focuses on their applications in medical imaging, genomics, natural language processing (NLP) for electronic health records, drug discovery and healthcare security. We present comparative insights between classical and quantum approaches such as slow processing of high-dimensional data, limited scalability and inefficiency in complex optimization problems. This review also emphasizes the emerging directions towards quantum-based personalized digital healthcare approaches. By combining medical science with quantum QML has the potential to revolutionize the future of precision diagnostics, treatment optimization and healthcare data security. This study also provides a valuable resource for those interested in quantum computing and researchers who want to stay updated on the fast-growing area.
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.
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.
This research project presents a PRISMA-guided systematic literature review exploring the application of quantum machine learning (QML) in oncology, a rapidly growing field with fragmented evidence. The study synthesizes peer-reviewed studies from six databases (ACM, IEEE, MDPI, Scopus, Wiley, and PubMed) published between 2020 and 2026, screening 212 records and selecting 49 studies for analysis. The review examines clinical tasks, cancer domains, QML architectures, challenges, and future directions, revealing that current research mainly focuses on classification and detection, particularly in breast cancer and brain tumors, using hybrid quantum-classical models. Despite promising results, progress is limited by hardware constraints, simulator dependence, and insufficient clinical validation. The findings provide a clear overview of current QML trends and highlight future priorities, including real quantum hardware testing, diverse clinical datasets, standardized benchmarking, and stronger collaboration between computing and oncology communities.
Healthcare 5.0 combines artificial intelligence, the Internet of Medical Things, edge and cloud computing, and digital twins to support safer, more personal, and more efficient care. Interest in quantum machine learning is growing in this space, but the current evidence is fragmented and hard to compare across studies. Many reports focus on small tasks, use weak or inconsistent baselines, and do not separate algorithmic gains from costs caused by data encoding, repeated circuit execution, and hardware noise. This survey addresses these gaps by providing a structured synthesis of Healthcare 5.0 applications and by offering a practical comparison between classical machine learning and quantum machine learning using criteria that matter for clinical use. We review representative use cases in medical imaging, electronic health record based prediction, remote monitoring, decision support, and digital twin enabled systems. We then analyze QML integration patterns that are realistic today, with emphasis on hybrid pipelines where classical models extract features and quantum components act as narrow decision or optimization modules. Finally, we consolidate the barriers that currently limit adoption, including encoding overhead, limited qubit counts and circuit depth, training instability and reproducibility, privacy and governance constraints, and the need for careful benchmarking and reporting. We conclude with guidance on evaluation and study design to support fair comparison, clearer clinical relevance, and better alignment with Healthcare 5.0 engineering requirements.
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.