Citation

Ghauth, Khairil Imran and Kustiawan, Yanche Ari (2026) Quantum Machine Learning in Oncology: A Systematic Review of Clinical Applications, Challenges, and Future Research Directions. Machine Learning and Knowledge Extraction, 8 (8). p. 242. ISSN 2504-4990

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	quantum machine learning (QML), oncology, cancer detection, cancer classification, quantum computing, quantum support vector machine (QSVM), quantum neural network (QNN), quantum convolutional neural network (QCNN), hybrid quantum-classical models, systematic literature review
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	03 Sep 2026 02:27
Last Modified:	03 Sep 2026 02:27
URII:	http://shdl.mmu.edu.my/id/eprint/16581

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