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Review
. 2023 Jan-Dec:22:15330338231215214.
doi: 10.1177/15330338231215214.

Oncological Applications of Quantum Machine Learning

Milad Rahimi  1 , Farkhondeh Asadi  1
Affiliations
Review

Oncological Applications of Quantum Machine Learning

Milad Rahimi et al. Technol Cancer Res Treat. 2023 Jan-Dec.

Abstract

Background: Cancer is a leading cause of death worldwide. Machine learning (ML) and quantum computers (QCs) have recently advanced significantly. Numerous studies have examined the application of quantum machine learning (QML) in healthcare and validated its superiority over classical ML algorithms. Objectives: This review investigates and reports the oncological applications of QML. Methods: In March 2023, an electronic investigation of PubMed, Scopus, Web of Science, IEEE, and Cochrane databases was performed. The articles were screened based on titles and abstracts, and their full texts were examined. Results: Initially, a total of 207 articles were retrieved. Thereafter, 9 articles were included in the study, most of which were published from 2020 onwards. The results indicated the implementation of various QML techniques in different aspects of oncology, such as reducing mammography image noise, edge detection of breast cancer, clinical decision support in radiotherapy treatment, and cancer classification. Conclusion: These studies revealed that integrating quantum science with ML can significantly improve patient care and clinical outcomes. Future studies should explore the integration of QC and ML and the development of novel algorithms to enhance cancer prognosis, diagnosis, and treatment planning.

Keywords: cancer; machine learning; oncology; quantum computer; quantum machine learning.

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Conflict of interest statement

Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Hybrid quantum convolutional neural networks architecture.
Figure 2.
Figure 2.
Flow diagram of the identification and selection process following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines.
See this image and copyright information in PMC

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