Oncological Applications of Quantum Machine Learning
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....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-12-01
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| Series: | Technology in Cancer Research & Treatment |
| Online Access: | https://doi.org/10.1177/15330338231215214 |
| _version_ | 1797386998780002304 |
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| author | Milad Rahimi MS Farkhondeh Asadi PhD |
| author_facet | Milad Rahimi MS Farkhondeh Asadi PhD |
| author_sort | Milad Rahimi MS |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T22:17:33Z |
| format | Article |
| id | doaj.art-9ea5aa0b45454a95953b84e32c191f7e |
| institution | Directory Open Access Journal |
| issn | 1533-0338 |
| language | English |
| last_indexed | 2024-03-08T22:17:33Z |
| publishDate | 2023-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Technology in Cancer Research & Treatment |
| spelling | doaj.art-9ea5aa0b45454a95953b84e32c191f7e2023-12-18T20:07:29ZengSAGE PublishingTechnology in Cancer Research & Treatment1533-03382023-12-012210.1177/15330338231215214Oncological Applications of Quantum Machine LearningMilad Rahimi MSFarkhondeh Asadi PhDBackground: 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.https://doi.org/10.1177/15330338231215214 |
| spellingShingle | Milad Rahimi MS Farkhondeh Asadi PhD Oncological Applications of Quantum Machine Learning Technology in Cancer Research & Treatment |
| title | Oncological Applications of Quantum Machine Learning |
| title_full | Oncological Applications of Quantum Machine Learning |
| title_fullStr | Oncological Applications of Quantum Machine Learning |
| title_full_unstemmed | Oncological Applications of Quantum Machine Learning |
| title_short | Oncological Applications of Quantum Machine Learning |
| title_sort | oncological applications of quantum machine learning |
| url | https://doi.org/10.1177/15330338231215214 |
| work_keys_str_mv | AT miladrahimims oncologicalapplicationsofquantummachinelearning AT farkhondehasadiphd oncologicalapplicationsofquantummachinelearning |