Agentic AI in Healthcare: Bridging the Gap Between Computational Promise and Clinical Evidence

Agentic AI in Healthcare: Bridging the Gap Between Computational Promise and Clinical Evidence | 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 Agentic AI in Healthcare: Bridging the Gap Between Computational Promise and Clinical Evidence Yunguo Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9374197/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 Background: Agentic AI systems are increasingly proposed for healthcare applications, yet the evidence base distinguishing computational promise from clinical reality remains poorly characterised. Single-agent systems offer efficiency for routine diagnostics; multi-agent systems promise robustness for complex care. Both face barriers in safety, accountability, and equitable deployment. Methods: We conducted a PRISMA-ScR scoping review synthesising evidence from 161 studies (January 2018–October 2024, with selective early-access coverage through April 2026) retrieved from PubMed, IEEE Xplore, arXiv, Google Scholar, and Scopus. Evidence certainty was graded using an adapted GRADEinformed framework appropriate for heterogeneous clinical and simulation evidence. Given substantial heterogeneity across architectures, tasks, and outcome measures, quantitative pooling was not appropriate; we employed structured evidence mapping and narrative synthesis. A pragmatic, deployment-focused definition of “agent” was adopted and extended with a five-level Agentic Capability Spectrum (Levels 0–4) to preserve discriminative power. Results: High-certainty evidence supports selected single-agent systems in specialised diagnostic domains (94.5% accuracy in retinal screening; AUC 0.96 in skin-cancer classification). Very low-certainty evidence from simulation studies suggests potential coordination advantages for multi-agent systems, with no confirmed clinical deployment. Multi-agent systems require substantially higher computational resources and introduce coordination latency (200–500 ms in simulation). Cross-cutting barriers include algorithmic bias in one commercial population-health algorithm (Moderate-certainty; Obermeyer et al., 2019; generalisability uncertain), unclear liability frameworks, and workflow-integration failures. Evidence is predominantly from high-income countries (87% of studies; descriptive evidence-mapping finding). Conclusions: Single-agent systems demonstrate validated clinical utility in constrained tasks, whereas multiagent systems remain experimental. Priorities include large-scale clinical trials for multi-agent architectures, standardised safety frameworks, risk-based regulatory pathways, and equity-focused global deployment strategies. As this synthesis was conducted by a single reviewer, all findings represent a preliminary structured synthesis requiring independent replication before informing clinical guideline development. Bioinformatics Artificial Intelligence and Machine Learning Artificial Intelligence Multi-Agent Systems Single-Agent Systems Agentic AI Clinical Decision Support Systems Scoping Review Healthcare Delivery Patient Safety Algorithmic Bias Implementation Science Medical Informatics Ethics Global Health 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-9374197","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":621425466,"identity":"6f9ec8ad-e471-43ef-9bf0-5e37742f380b","order_by":0,"name":"Yunguo Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDADfhCRUECKFskGkBYDUrQYHACTxKi8kWMmXfDrsJzx+dWJHx4YMMjzix0gQsvMvsPGZjfebpYAOsxw5uwEIrTw9hxO3Hbj7AaQlgSD28Rq2Tzj7OYfxGvh+XE4cQN/7zbibJE886zYmrch3VjiBu82iwQDCcJ+4TuevPE2zx9rOf7+s5tv/qiwkeeXJqBF4QCHAQNjG5AlAVYpgV85CMg3sD9gYPgDZPEfIKx6FIyCUTAKRiYAAAlWRzOowwZ5AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0006-2003-3813","institution":"Zyter|TruCare","correspondingAuthor":true,"prefix":"","firstName":"Yunguo","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2026-04-10 03:57:29","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9374197/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9374197/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106961177,"identity":"80c1fe88-8476-4dd7-81aa-d871d19f82d1","added_by":"auto","created_at":"2026-04-15 09:24:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":675955,"visible":true,"origin":"","legend":"","description":"","filename":"AgenticAIinHealthcareReview.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9374197/v1_covered_2fb3ecdd-c810-49e4-8617-c6abc7269df6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAgentic AI in Healthcare: Bridging the Gap Between Computational Promise and Clinical Evidence\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Zyter|TruCare","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":"Artificial Intelligence, Multi-Agent Systems, Single-Agent Systems, Agentic AI, Clinical Decision Support Systems, Scoping Review, Healthcare Delivery, Patient Safety, Algorithmic Bias, Implementation Science, Medical Informatics, Ethics, Global Health","lastPublishedDoi":"10.21203/rs.3.rs-9374197/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9374197/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Agentic AI systems are increasingly proposed for healthcare applications, yet the evidence base distinguishing computational promise from clinical reality remains poorly characterised. Single-agent systems offer efficiency for routine diagnostics; multi-agent systems promise robustness for complex care. Both face barriers in safety, accountability, and equitable deployment.\u003c/p\u003e\n\u003cp\u003eMethods: We conducted a PRISMA-ScR scoping review synthesising evidence from 161 studies (January 2018–October 2024, with selective early-access coverage through April 2026) retrieved from PubMed, IEEE Xplore, arXiv, Google Scholar, and Scopus. Evidence certainty was graded using an adapted GRADEinformed framework appropriate for heterogeneous clinical and simulation evidence. Given substantial heterogeneity across architectures, tasks, and outcome measures, quantitative pooling was not appropriate; we employed structured evidence mapping and narrative synthesis. A pragmatic, deployment-focused definition of “agent” was adopted and extended with a five-level Agentic Capability Spectrum (Levels 0–4) to preserve discriminative power.\u003c/p\u003e\n\u003cp\u003eResults: High-certainty evidence supports selected single-agent systems in specialised diagnostic domains (94.5% accuracy in retinal screening; AUC 0.96 in skin-cancer classification). Very low-certainty evidence from simulation studies suggests potential coordination advantages for multi-agent systems, with no confirmed clinical deployment. Multi-agent systems require substantially higher computational resources and introduce coordination latency (200–500 ms in simulation). Cross-cutting barriers include algorithmic bias in one commercial population-health algorithm (Moderate-certainty; Obermeyer et al., 2019; generalisability uncertain), unclear liability frameworks, and workflow-integration failures. Evidence is predominantly from high-income countries (87% of studies; descriptive evidence-mapping finding).\u003c/p\u003e\n\u003cp\u003eConclusions: Single-agent systems demonstrate validated clinical utility in constrained tasks, whereas multiagent systems remain experimental. Priorities include large-scale clinical trials for multi-agent architectures, standardised safety frameworks, risk-based regulatory pathways, and equity-focused global deployment strategies. 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