Large Language and Generative Foundation Models for Cloud-Enabled Medical Imaging and Internet of Medical Things: A PRISMA Systematic Review of Architectures, Security, and Deployment

Large Language and Generative Foundation Models for Cloud-Enabled Medical Imaging and Internet of Medical Things: A PRISMA Systematic Review of Architectures, Security, and Deployment | 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 Large Language and Generative Foundation Models for Cloud-Enabled Medical Imaging and Internet of Medical Things: A PRISMA Systematic Review of Architectures, Security, and Deployment Behnam Kiani Kalejahi, Kamila Khalimova, Mohammad Javad Rajabi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8859031/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Large Language Models (LLMs) are becoming increasingly entwined in the cloud-based processing of medical images, enabling functions such as cross-modal synthesis, text generation, and decision support systems. In some of the earliest studies, the use of generative adversarial models (GANs) demonstrated the potential for MRI-to-CT transfer, and the synthesis of T1 and T2 images, and the ability to translate images without supervision using models such as CycleGAN. More recently, the approaches include the use of diffusion models, and the combination of GAN models and diffusion models, and multimodal models that enable the processing of imaging and clinical text simultaneously. Despite the progress, the key challenges include maintaining the consistency of the volumetric images for three-dimensional models, domain transfer between different scanners, and the need for models to enable reasoning for clinical interpretation. Using the systematic review approach based on the guidelines of the PRISMA protocol, the study reviews the literature regarding the differences and similarities between lightweight and complex models for the translation of images in the cloud-based and Internet of Medical Things (IoMT) systems, and the suitability of the models for implementation. Future studies will include the design of two-dimensional and three-dimensional models, and the development of models for distributed processing, which enable models to be scaled for different institutions for clinical decision-making. The study results provide conclusive insights into the fundamental trade-off between the two models, and the pivotal part of the new generation of models based on the design of the LLM systems. Large Language Models Cloud Computing Multimodal AI Hybrid Architectures Federated Learning Generative AI Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers invited by journal 03 Mar, 2026 Editor invited by journal 26 Feb, 2026 Editor assigned by journal 15 Feb, 2026 Submission checks completed at journal 15 Feb, 2026 First submitted to journal 12 Feb, 2026 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-8859031","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600315550,"identity":"2b317c45-bdbd-4a10-9c0d-8d23b5a2b6ec","order_by":0,"name":"Behnam Kiani Kalejahi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACAwY2hgMMDBJyUL4FAwMzkVqMgWzGBiCDOC0gkNgA10IImDOwJR74ucMivb+99/jjihoJefl25mcPGNvqcGqxbGA7cLD3jETujDPnEhvPHJMw3HCYzdyAsY0Nt8MOsDcc4G2TyN0gkWPY2MAmwbiBmcFMgrGNB6+Wg3/bJNIN5N8AtfyTsJ/fzP4NqAW3pwwOsB04DLQlwUCCx7CxsU0iseEwD8gWA9x+aWZLOCzbJmE440yO4czGPonkDYd5yiQSziXg1GLO3mb88W1bnTx/+xmDjw3fbGzn9x/fJvGhDHeI4Yg43HaMglEwCkbBKCAGAABs7U6lQyMaVgAAAABJRU5ErkJggg==","orcid":"","institution":"Central Asian University","correspondingAuthor":true,"prefix":"","firstName":"Behnam","middleName":"Kiani","lastName":"Kalejahi","suffix":""},{"id":600315551,"identity":"fcf27560-ce19-4236-866a-c466a63c227a","order_by":1,"name":"Kamila Khalimova","email":"","orcid":"","institution":"Central Asian University","correspondingAuthor":false,"prefix":"","firstName":"Kamila","middleName":"","lastName":"Khalimova","suffix":""},{"id":600315552,"identity":"28f69824-9a81-4b87-a9f5-e8280e1def38","order_by":2,"name":"Mohammad Javad Rajabi","email":"","orcid":"","institution":"INTI International University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Javad","lastName":"Rajabi","suffix":""}],"badges":[],"createdAt":"2026-02-12 07:40:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8859031/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8859031/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104403791,"identity":"5e31e76e-6107-49c0-ac66-ad1c2e0decab","added_by":"auto","created_at":"2026-03-11 12:19:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":768035,"visible":true,"origin":"","legend":"","description":"","filename":"LargeLanguageModelsforCloud.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8859031/v1_covered_209dfcf9-6320-47ec-8895-e7de4d06fbe7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Large Language and Generative Foundation Models for Cloud-Enabled Medical Imaging and Internet of Medical Things: A PRISMA Systematic Review of Architectures, Security, and Deployment","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"Large Language Models, Cloud Computing, Multimodal AI, Hybrid Architectures, Federated Learning, Generative AI","lastPublishedDoi":"10.21203/rs.3.rs-8859031/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8859031/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLarge Language Models (LLMs) are becoming increasingly entwined in the cloud-based processing of medical images, enabling functions such as cross-modal synthesis, text generation, and decision support systems. In some of the earliest studies, the use of generative adversarial models (GANs) demonstrated the potential for MRI-to-CT transfer, and the synthesis of T1 and T2 images, and the ability to translate images without supervision using models such as CycleGAN. More recently, the approaches include the use of diffusion models, and the combination of GAN models and diffusion models, and multimodal models that enable the processing of imaging and clinical text simultaneously. Despite the progress, the key challenges include maintaining the consistency of the volumetric images for three-dimensional models, domain transfer between different scanners, and the need for models to enable reasoning for clinical interpretation. Using the systematic review approach based on the guidelines of the PRISMA protocol, the study reviews the literature regarding the differences and similarities between lightweight and complex models for the translation of images in the cloud-based and Internet of Medical Things (IoMT) systems, and the suitability of the models for implementation. Future studies will include the design of two-dimensional and three-dimensional models, and the development of models for distributed processing, which enable models to be scaled for different institutions for clinical decision-making. The study results provide conclusive insights into the fundamental trade-off between the two models, and the pivotal part of the new generation of models based on the design of the LLM systems.\u003c/p\u003e","manuscriptTitle":"Large Language and Generative Foundation Models for Cloud-Enabled Medical Imaging and Internet of Medical Things: A PRISMA Systematic Review of Architectures, Security, and Deployment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 17:38:00","doi":"10.21203/rs.3.rs-8859031/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-24T05:41:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T06:42:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T05:04:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259251580947532640674160644841766186311","date":"2026-04-19T04:31:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209605267734748890982465187917508778060","date":"2026-04-13T08:58:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-11T08:43:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T12:17:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206163435261524029490375758154167200441","date":"2026-03-04T03:30:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154285883443683585118734350369639376670","date":"2026-03-03T18:33:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-03T14:56:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-26T08:53:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T04:59:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T04:58:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Artificial Intelligence","date":"2026-02-12T07:36:10+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"c1d50765-3595-461f-8bf9-ba0a392afe76","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T11:08:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-06 17:38:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8859031","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8859031","identity":"rs-8859031","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}