A systematic review of quantum machine learning for digital health
Abstract 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. I...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01597-z |
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| _version_ | 1849314875782725632 |
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| author | Riddhi S. Gupta Carolyn E. Wood Teyl Engstrom Jason D. Pole Sally Shrapnel |
| author_facet | Riddhi S. Gupta Carolyn E. Wood Teyl Engstrom Jason D. Pole Sally Shrapnel |
| author_sort | Riddhi S. Gupta |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-5e9333db910045f88b9cc251b3fdb791 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-5e9333db910045f88b9cc251b3fdb7912025-08-20T03:52:19ZengNature Portfolionpj Digital Medicine2398-63522025-05-018111510.1038/s41746-025-01597-zA systematic review of quantum machine learning for digital healthRiddhi S. Gupta0Carolyn E. Wood1Teyl Engstrom2Jason D. Pole3Sally Shrapnel4School of Mathematics and Physics, The University of QueenslandSchool of Mathematics and Physics, The University of QueenslandQDHeC. Centre for Health Services Research. Faculty of Medicine, The University of QueenslandQDHeC. Centre for Health Services Research. Faculty of Medicine, The University of QueenslandSchool of Mathematics and Physics, The University of QueenslandAbstract 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.https://doi.org/10.1038/s41746-025-01597-z |
| spellingShingle | Riddhi S. Gupta Carolyn E. Wood Teyl Engstrom Jason D. Pole Sally Shrapnel A systematic review of quantum machine learning for digital health npj Digital Medicine |
| title | A systematic review of quantum machine learning for digital health |
| title_full | A systematic review of quantum machine learning for digital health |
| title_fullStr | A systematic review of quantum machine learning for digital health |
| title_full_unstemmed | A systematic review of quantum machine learning for digital health |
| title_short | A systematic review of quantum machine learning for digital health |
| title_sort | systematic review of quantum machine learning for digital health |
| url | https://doi.org/10.1038/s41746-025-01597-z |
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