Quantum Machine Learning Approaches for Medical Image Analysis: Methods and Diagnostic Applications

Quantum Machine Learning Approaches for Medical Image Analysis: Methods and Diagnostic Applications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantum Machine Learning Approaches for Medical Image Analysis: Methods and Diagnostic Applications Ahmed Bin Osman, Ayomide owolabi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8852812/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The convergence of quantum computing and medical image analysis has emerged as a promising frontier for advancing diagnostic intelligence beyond the limits of classical machine learning. Recent developments in quantum hardware and quantum machine learning (QML) algorithms have stimulated growing interest in their potential to enhance complex medical imaging tasks, including disease classification, image segmentation, and clinical decision support (Wei et al., 2023; Ullah and Garcia-Zapirain, 2024). This study presents a comprehensive review and synthesis of state-of-the-art QML methodologies applied to medical imaging, systematically examining both theoretical foundations and empirical implementations. We analyze quantum-based and hybrid quantum–classical models employed across modalities such as MRI, CT, X-ray, and histopathological imaging, with particular attention to classification accuracy, computational efficiency, and diagnostic robustness (Maheshwari et al., 2022; ElBedoui et al., 2025). Our findings reveal clear trends indicating that hybrid quantum–classical approaches currently dominate practical applications, consistently outperforming purely classical baselines in select low-dimensional and domain-specific diagnostic tasks. Performance gains are most evident in feature-rich yet data-constrained scenarios, where quantum kernels and variational circuits demonstrate enhanced representational capacity (Ajlouni et al., 2023; Landman et al., 2022). However, these advantages remain context-dependent and sensitive to noise, circuit depth, and encoding strategies. Overall, QML holds significant promise for improving medical image-based diagnosis, but its widespread clinical adoption is constrained by hardware scalability, limited qubit coherence, and unresolved data encoding challenges. Addressing these limitations will be critical for translating QML from experimental feasibility to routine medical practice (Marengo and Santamato, 2025; Rawas and AlSaeed, 2026). Artificial Intelligence and Machine Learning Quantum machine learning medical image analysis Quantum-enhanced diagnostics Hybrid quantum–classical models Healthcare artificial intelligence Quantum neural networks Clinical decision support Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8852812","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589696670,"identity":"ce541e26-e13b-43b8-be4f-68044c76abf6","order_by":0,"name":"Ahmed Bin Osman","email":"","orcid":"https://orcid.org/0009-0008-1219-1453","institution":"Analytics, Campbellsville University, Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Bin","lastName":"Osman","suffix":""},{"id":589696671,"identity":"55a5caa3-ee96-462e-81b1-5dfd9e3dfe52","order_by":1,"name":"Ayomide owolabi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYLCCBDYQmdjA8AFIsbGTooVxBkgLM1HWgLUkMDDzgGhCWvhnJB/d8KDMJpqfPbnxs82vbfJ8zAyMHz7m4NYicSMt7UbCubTcmT0Pm6Vz+24btjEzMEvO3IbHmhs5ZjcS2w7nbgCSzLk9txmBWtiYefFokYdo+Z+7H6TFsue2PUEtBhAtB3I3SAC1MPy4nUhQi+GZZyC/JOfOOPOwWbK34XZyGzNjM16/yB1PPnbzR5ldbn97+sMPP/7ctp3f3nzww0d83hdIQOIwtoHJBjzqgYD/ADLvD37Fo2AUjIJRMDIBABtAWUqsAw1LAAAAAElFTkSuQmCC","orcid":"","institution":"Ladoke Akintola University Of Technology Ogbomosho","correspondingAuthor":true,"prefix":"","firstName":"Ayomide","middleName":"","lastName":"owolabi","suffix":""}],"badges":[],"createdAt":"2026-02-11 14:25:51","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8852812/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8852812/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102746507,"identity":"87f93d3f-66ae-4fdc-902c-4b9efd76b7ca","added_by":"auto","created_at":"2026-02-16 08:57:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":462693,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptABO1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8852812/v1_covered_6a8f2b77-a341-490c-966d-11be6f8a56aa.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eQuantum Machine Learning Approaches for Medical Image Analysis: Methods and Diagnostic Applications\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Analytics, Campbellsville University, Kentucky","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Quantum machine learning, medical image analysis, Quantum-enhanced diagnostics, Hybrid quantum–classical models, Healthcare artificial intelligence, Quantum neural networks, Clinical decision support","lastPublishedDoi":"10.21203/rs.3.rs-8852812/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8852812/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe convergence of quantum computing and medical image analysis has emerged as a promising frontier for advancing diagnostic intelligence beyond the limits of classical machine learning. Recent developments in quantum hardware and quantum machine learning (QML) algorithms have stimulated growing interest in their potential to enhance complex medical imaging tasks, including disease classification, image segmentation, and clinical decision support (Wei et al., 2023; Ullah and Garcia-Zapirain, 2024). This study presents a comprehensive review and synthesis of state-of-the-art QML methodologies applied to medical imaging, systematically examining both theoretical foundations and empirical implementations. We analyze quantum-based and hybrid quantum–classical models employed across modalities such as MRI, CT, X-ray, and histopathological imaging, with particular attention to classification accuracy, computational efficiency, and diagnostic robustness (Maheshwari et al., 2022; ElBedoui et al., 2025). Our findings reveal clear trends indicating that hybrid quantum–classical approaches currently dominate practical applications, consistently outperforming purely classical baselines in select low-dimensional and domain-specific diagnostic tasks. Performance gains are most evident in feature-rich yet data-constrained scenarios, where quantum kernels and variational circuits demonstrate enhanced representational capacity (Ajlouni et al., 2023; Landman et al., 2022). However, these advantages remain context-dependent and sensitive to noise, circuit depth, and encoding strategies.\u003c/p\u003e\n\u003cp\u003eOverall, QML holds significant promise for improving medical image-based diagnosis, but its widespread clinical adoption is constrained by hardware scalability, limited qubit coherence, and unresolved data encoding challenges. 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