Trustworthy and Ethical AI for Intrusion Detection in Healthcare IoT (IoMT) Systems: An Agentic Decision Loop Framework

Trustworthy and Ethical AI for Intrusion Detection in Healthcare IoT (IoMT) Systems: An Agentic Decision Loop Framework | 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 Article Trustworthy and Ethical AI for Intrusion Detection in Healthcare IoT (IoMT) Systems: An Agentic Decision Loop Framework IBRAHIM ADABARA, Bashir Olaniyi Sadiq, Aliyu Nuhu Shuaibu, Yale Ibrahim Danjuma, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9081737/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract The rapid expansion of Internet of Medical Things (IoMT) ecosystems has intensified cybersecurity challenges in healthcare settings, where network disruptions can compromise clinical safety and operational continuity. Traditional intrusion detection systems (IDS) often achieve high classification accuracy but remain vulnerable to unsafe behaviors, including false escalations, excessive blocking, and inconsistent threat triage. This study proposes a trustworthy and ethically aligned multi-agent IDS framework for healthcare environments, integrating a calibrated supervised detector, a Deep Q-Network (DQN) triage agent, and a governance layer grounded in the NIST AI Risk Management Framework. The framework is evaluated using the CIC-IoMT 2024 dataset for in-domain training, the CSE-CIC-IDS2018 dataset for domain-shift testing, and contextual clinical indicators derived from the MIMIC-IV database. To comprehensively assess ethical and operational reliability, the study introduces four novel governance metrics: Ethical Compliance Rate (ECR), Governance Compliance Index (GCI), False Escalation Rate (FER), and Cross-Domain Adaptation Score (CAS). Experimental results demonstrate strong performance, with an accuracy of 0.983, a weighted F1-score of 0.978, an ECR of 0.990, and a FER of 0.021, indicating high compliance with safety and proportionality constraints. Compared to baseline classifiers, including standard Random Forest and gradient boosting models, the proposed framework exhibits superior adaptability and governance alignment under domain shift conditions. These findings underscore the value of embedding ethical oversight and operational context into reinforcement learning to enable safer, more resilient, and transparent intrusion detection in real-world healthcare IoT deployments. The implementation code is publicly available at: https://doi.org/10.6084/m9.figshare.30686600 . Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Trustworthy AI Reinforcement Learning Intrusion Detection System (IDS) Internet of Medical Things (IoMT) Ethical AI Cybersecurity NIST AI Risk Management Framework Healthcare Networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 07 May, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor invited by journal 16 Mar, 2026 Editor assigned by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 10 Mar, 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. 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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-9081737","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":621586359,"identity":"42bfc752-c803-442e-8cf9-7ffdbf908f85","order_by":0,"name":"IBRAHIM 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Traditional intrusion detection systems (IDS) often achieve high classification accuracy but remain vulnerable to unsafe behaviors, including false escalations, excessive blocking, and inconsistent threat triage. This study proposes a trustworthy and ethically aligned multi-agent IDS framework for healthcare environments, integrating a calibrated supervised detector, a Deep Q-Network (DQN) triage agent, and a governance layer grounded in the NIST AI Risk Management Framework. The framework is evaluated using the CIC-IoMT 2024 dataset for in-domain training, the CSE-CIC-IDS2018 dataset for domain-shift testing, and contextual clinical indicators derived from the MIMIC-IV database. To comprehensively assess ethical and operational reliability, the study introduces four novel governance metrics: Ethical Compliance Rate (ECR), Governance Compliance Index (GCI), False Escalation Rate (FER), and Cross-Domain Adaptation Score (CAS). Experimental results demonstrate strong performance, with an accuracy of 0.983, a weighted F1-score of 0.978, an ECR of 0.990, and a FER of 0.021, indicating high compliance with safety and proportionality constraints. Compared to baseline classifiers, including standard Random Forest and gradient boosting models, the proposed framework exhibits superior adaptability and governance alignment under domain shift conditions. These findings underscore the value of embedding ethical oversight and operational context into reinforcement learning to enable safer, more resilient, and transparent intrusion detection in real-world healthcare IoT deployments. 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