Automated AI Model Development: a Systematic Literature Review

Automated AI Model Development: a Systematic Literature Review | 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 Automated AI Model Development: a Systematic Literature Review Sánchez Pérez, Álvaro, Gaya Lopez, María Cruz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7394040/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 Artificial intelligence (AI) has rapidly evolved, presenting both opportunities and challenges in the development and deployment of sophisticated models. Traditional methods for creating AI models often require extensive human expertise for tasks such as feature engineering, hyperparameter tuning, and model selection, making the process time-consuming and prone to bias. In response, automation techniques-including automated machine learning (AutoML), hyperparameter optimization, and neural architecture search-have emerged to streamline model generation. This systematic literature review explores current trends, benefits, and challenges associated with AI model automation. Drawing upon articles published from 2020 onwards, the review follows the CIMO (Context, Intervention, Mechanism, Outcome) framework and adheres to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure methodological rigor. Searches were conducted in Scopus, Web of Science, and Academic Search Ultimate, focusing on peer-reviewed studies that address AI model automation in supervised, unsupervised, and reinforcement learning domains. Each study's context, intervention techniques, underlying mechanisms, and outcomes were extracted and assessed for quality and relevance. By synthesizing the findings of recent research, this review not only highlights advancements in automated AI model development but also identifies gaps in existing knowledge. The results provide critical insights for researchers and practitioners, guiding future exploration of scalable, efficient, and reliable AI automation strategies. Automated Machine Learning (AutoML) AI Model Automation Hyperparameter Optimization Neural Architecture Search Systematic Literature Review Scalable AI Systems Full Text Additional Declarations No competing interests reported. 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-7394040","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":567538342,"identity":"49a5ba01-8103-4259-9687-bad0ec79abd7","order_by":0,"name":"Sánchez Pérez, Álvaro","email":"data:image/png;base64,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","orcid":"","institution":"Universidad Europea de Madrid","correspondingAuthor":true,"prefix":"","firstName":"Álvaro","middleName":"Sánchez","lastName":"Pérez","suffix":""},{"id":567538346,"identity":"597f7d36-ff2e-4606-a9ea-1a12784830f5","order_by":1,"name":"Gaya Lopez, María Cruz","email":"","orcid":"","institution":"Universidad Europea de Madrid","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Cruz Gaya","lastName":"Lopez","suffix":""}],"badges":[],"createdAt":"2025-08-17 18:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7394040/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7394040/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104501612,"identity":"1f715597-e8fd-4ae4-8bba-5e3e486d48db","added_by":"auto","created_at":"2026-03-12 13:56:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":421973,"visible":true,"origin":"","legend":"","description":"","filename":"SanchezGayaAIliteraturereview.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7394040/v1_covered_7428513d-49aa-400f-bb98-11bc0ee2d15a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Automated AI Model Development: a Systematic Literature Review","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":" Automated Machine Learning (AutoML), AI Model Automation, Hyperparameter Optimization, Neural Architecture Search, Systematic Literature Review, Scalable AI Systems","lastPublishedDoi":"10.21203/rs.3.rs-7394040/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7394040/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Artificial intelligence (AI) has rapidly evolved, presenting both opportunities and challenges in the development and deployment of sophisticated models. 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