{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T20:48:28Z","timestamp":1785962908820,"version":"3.56.0"},"reference-count":113,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,9]],"date-time":"2025-03-09T00:00:00Z","timestamp":1741478400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Government of Canada\u2019s New Frontiers in Research Fund\u2014Exploration","award":["NFRFE-2022-00707"],"award-info":[{"award-number":["NFRFE-2022-00707"]}]},{"name":"Government of Canada\u2019s New Frontiers in Research Fund\u2014Exploration","award":["CHIR PCS\u2013191021"],"award-info":[{"award-number":["CHIR PCS\u2013191021"]}]},{"name":"Planning and Dissemination Grant\u2014Institute Community Support, Canadian Institutes of Health Research, Canada","award":["NFRFE-2022-00707"],"award-info":[{"award-number":["NFRFE-2022-00707"]}]},{"name":"Planning and Dissemination Grant\u2014Institute Community Support, Canadian Institutes of Health Research, Canada","award":["CHIR PCS\u2013191021"],"award-info":[{"award-number":["CHIR PCS\u2013191021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Medical decision-making is increasingly integrating quantum computing (QC) and machine learning (ML) to analyze complex datasets, improve diagnostics, and enable personalized treatments. While QC holds the potential to accelerate optimization, drug discovery, and genomic analysis as hardware capabilities advance, current implementations remain limited compared to classical computing in many practical applications. Meanwhile, ML has already demonstrated significant success in medical imaging, predictive modeling, and decision support. Their convergence, particularly through quantum machine learning (QML), presents opportunities for future advancements in processing high-dimensional healthcare data and improving clinical outcomes. This review examines the foundational concepts, key applications, and challenges of these technologies in healthcare, explores their potential synergy in solving clinical problems, and outlines future directions for quantum-enhanced ML in medical decision-making.<\/jats:p>","DOI":"10.3390\/a18030156","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T05:46:52Z","timestamp":1741585612000},"page":"156","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":82,"title":["Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4202-4855","authenticated-orcid":false,"given":"James C. L.","family":"Chow","sequence":"first","affiliation":[{"name":"Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1X6, Canada"},{"name":"Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada"},{"name":"Department of Materials Science and Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1046\/j.1365-2753.2001.00284.x","article-title":"Rationality in medical decision making: A review of the literature on doctors\u2019 decision-making biases","volume":"7","author":"Bornstein","year":"2001","journal-title":"J. Eval. Clin. 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