Quantum machine learning revolution in healthcare: a systematic review of emerging perspectives and applications

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dc.contributor.authorUllah, Ubaid
dc.contributor.authorGarcía-Zapirain, Begoña
dc.date.accessioned2025-03-26T14:16:33Z
dc.date.available2025-03-26T14:16:33Z
dc.date.issued2024
dc.date.updated2025-03-26T14:16:33Z
dc.description.abstractQuantum computing (QC) stands apart from traditional computing systems by employing revolutionary techniques for processing information. It leverages the power of quantum bits (qubits) and harnesses the unique properties exhibited by subatomic particles, such as superposition, entanglement, and interference. These quantum phenomena enable quantum computers to operate on an entirely different level, exponentially surpassing the computational capabilities of classical computers. By manipulating qubits and capitalising on their quantum states, QC holds the promise of solving complex problems that are currently intractable in the case of traditional computers. The potential impact of QC extends beyond its computational power and reaches into various critical sectors, including healthcare. Scientists and engineers are working diligently to overcome various challenges and limitations associated with QC technology. These include issues related to qubit stability, error correction, scalability, and noise reduction. In such a scenario, our proposed work provides a concise summary of the most recent state of the art based on articles published between 2018 and 2023 in the healthcare domain. Additionally, the approach follows the necessary guidelines for conducting a systematic literature review. This includes utilising research questions and evaluating the quality of the articles using specific metrics. Initially, a total of 2,038 records were acquired from multiple databases, with 468 duplicate records and 1,053 records unrelated to healthcare subsequently excluded. A further 258, 68, and 39 records were eliminated based on title, abstract, and full-text criteria, respectively. Ultimately, the remaining 49 articles were subject to evaluation, thus providing a brief overview of the recent literature and contributing to existing knowledge and comprehension of Quantum Machine Learning (QML) algorithms and their applications in the healthcare sector. This analysis establishes a foundational framework for forthcoming research and development at the intersection of QC and machine learning, ultimately paving the way for innovative approaches to addressing complex challenges within the healthcare domainen
dc.description.sponsorshipThis work was supported in part by the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie under Grant 847624; and in part by the eVIDA Research Group, University of Deusto, Bilbao, Spain, through the Basque Governmenten
dc.identifier.citationUllah, U., & Garcia-Zapirain, B. (2024). Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications. IEEE Access, 12, 11423-11450. https://doi.org/10.1109/ACCESS.2024.3353461
dc.identifier.doi10.1109/ACCESS.2024.3353461
dc.identifier.eissn2169-3536
dc.identifier.urihttps://hdl.handle.net/20.500.14454/2577
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights© 2024 The Authors
dc.subject.otherHealthcare
dc.subject.otherQuantum computing
dc.subject.otherQuantum machine learning algorithms
dc.subject.otherSystematic review
dc.titleQuantum machine learning revolution in healthcare: a systematic review of emerging perspectives and applicationsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage11450
oaire.citation.startPage11423
oaire.citation.titleIEEE Access
oaire.citation.volume12
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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