Understanding the Landscape: A Review of Explainable AI in Healthcare Decision-Making

Understanding the Landscape: A Review of Explainable AI in Healthcare Decision-Making | 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 Systematic Review Understanding the Landscape: A Review of Explainable AI in Healthcare Decision-Making Zulfikar Ali Ansari, Manish Madhava Tripathi, Rafeeq Ahmed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4908320/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 May, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted 11 You are reading this latest preprint version Abstract Breast cancer remains a significant global health concern, impacting millions of women. Early and accurate diagnosis is crucial for improving treatment outcomes and reducing mortality rates. Machine learning (ML) has emerged as a powerful tool for breast cancer prediction, demonstrating its ability to identify complex patterns and relationships in large datasets. This paves the way for efficient collaboration between AI and healthcare professionals. This systematic review explores the diverse machine-learning techniques employed in breast cancer diagnosis. We comprehensively analyse and evaluate the effectiveness of various computational methodologies by synthesising findings from a wide range of peer-reviewed studies. Our analysis highlights the substantial advancements achieved in utilizing machine learning algorithms for breast cancer prediction. However, challenges remain in harnessing the full potential of machine learning for healthcare. These include the need for larger and more diverse datasets, the effective incorporation of imaging data, and the development of interpretable models. While AI offers immense potential for improving healthcare, ensuring transparency, interpretability, and trust is crucial, especially in complex domains like cancer diagnosis. This research emphasizes the importance of Explainable AI (XAI) for enhancing clinical decision-making and building trust between patients and healthcare providers. We advocate for fostering interdisciplinary collaboration among AI researchers, medical professionals, ethicists, and policymakers to ensure the responsible integration of AI in healthcare. Explainable AI Artificial Intelligence Healthcare Machine Learning Deep Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 May, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Revision requested 18 Sep, 2024 Reviews received at journal 15 Sep, 2024 Reviews received at journal 07 Sep, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviewers agreed at journal 27 Aug, 2024 Reviewers agreed at journal 27 Aug, 2024 Reviewers agreed at journal 27 Aug, 2024 Reviewers invited by journal 27 Aug, 2024 Editor assigned by journal 21 Aug, 2024 Submission checks completed at journal 19 Aug, 2024 First submitted to journal 13 Aug, 2024 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. 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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-4908320","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":354283228,"identity":"6470508f-9673-4c96-9f12-106713b6a46b","order_by":0,"name":"Zulfikar Ali Ansari","email":"","orcid":"","institution":"Integral University","correspondingAuthor":false,"prefix":"","firstName":"Zulfikar","middleName":"Ali","lastName":"Ansari","suffix":""},{"id":354283229,"identity":"aafbb8cf-f78d-4810-91c4-971571550f0a","order_by":1,"name":"Manish Madhava Tripathi","email":"","orcid":"","institution":"Integral University","correspondingAuthor":false,"prefix":"","firstName":"Manish","middleName":"Madhava","lastName":"Tripathi","suffix":""},{"id":354283230,"identity":"ad5f9310-dcbc-47c0-99cc-898f27452508","order_by":2,"name":"Rafeeq Ahmed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYFACxgdQBvMBICEhQ4QWZgMogy0BpIWHFC08YAZhLebthxkffChjkNedkfP51Y0aCx4G9sNHN+DTInMmmdlwxjkGw203crdZ5xwDOownLe0GPi0SDPnHpHnbGBhBWoxz2IBaJHjM8Gvhf8z++28bg/22GznPjHP+EaNFIpmNmbGNIRGohflxbhtRWh4zS/ack0jeduaZGXNunwQPG0G/8CczfvhRZmO77Xjy48853+rk+NkPH8OrBQLYJJBIwsoRypg/EKd6FIyCUTAKRhoAABPGQx0cB//aAAAAAElFTkSuQmCC","orcid":"","institution":"Government Engineering College West Champaran","correspondingAuthor":true,"prefix":"","firstName":"Rafeeq","middleName":"","lastName":"Ahmed","suffix":""}],"badges":[],"createdAt":"2024-08-13 15:52:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4908320/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4908320/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s44163-025-00307-8","type":"published","date":"2025-05-26T15:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83782978,"identity":"8d7f1ee3-4407-429f-addd-9e328e6b01dc","added_by":"auto","created_at":"2025-06-02 16:09:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1529044,"visible":true,"origin":"","legend":"","description":"","filename":"XAIMLReviewPaper13082024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4908320/v1_covered_9da7af93-c0b9-4d46-a7d3-635f39fb2d1e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding the Landscape: A Review of Explainable AI in Healthcare Decision-Making","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Explainable AI, Artificial Intelligence, Healthcare, Machine Learning, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-4908320/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4908320/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast cancer remains a significant global health concern, impacting millions of women. 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