Sensitive Patient Data Research Articles

The Data Heterogeneity Issue Regarding COVID-19 Lung Imaging in Federated Learning: An Experimental Study

Federated learning (FL) has emerged as a transformative framework for collaborative learning, offering robust model training across institutions while ensuring data privacy. In the context of making a COVID-19 diagnosis using lung imaging, FL enables institutions to collaboratively train a global model without sharing sensitive patient data. A central manager aggregates local model updates to compute global updates, ensuring secure and effective integration. The global model’s generalization capability is evaluated using centralized testing data before dissemination to participating nodes, where local assessments facilitate personalized adaptations tailored to diverse datasets. Addressing data heterogeneity, a critical challenge in medical imaging, is essential for improving both global performance and local personalization in FL systems. This study emphasizes the importance of recognizing real-world data variability before proposing solutions to tackle non-independent and non-identically distributed (non-IID) data. We investigate the impact of data heterogeneity on FL performance in COVID-19 lung imaging across seven distinct heterogeneity settings. By comprehensively evaluating models using generalization and personalization metrics, we highlight challenges and opportunities for optimizing FL frameworks. The findings provide valuable insights that can guide future research toward achieving a balance between global generalization and local adaptation, ultimately enhancing diagnostic accuracy and patient outcomes in COVID-19 lung imaging.

Navigating the Intersection of Digital Security, Resilience and Sustainability in Healthcare: A Theoretical Framework and Case Study of Ghana

This paper explores the intersection of digital security, resilience, and sustainability within healthcare systems, providing a theoretical framework and insights from a case study of Ghana. The findings reveal that integrating robust cybersecurity measures enhances the protection of sensitive patient data and ensures the reliability of clinical operations. These measures, supported by resilient infrastructures, mitigate disruptions and maintain service continuity, aligning healthcare systems with global ethical and security standards. The study further demonstrates that sustainable digital transformation strategies, such as adopting energy-efficient data centers and reducing reliance on paper-based workflows, significantly reduce environmental impacts while improving operational efficiency. These practices contribute to achieving Sustainable Development Goal 13 (Climate Action) and underscore the importance of aligning healthcare innovations with ecological stewardship. Lastly, the research highlights the critical role of environmental and social safeguards in fostering equitable healthcare digital transformation. Initiatives targeting compliance with environmental regulations and reducing digital disparities are shown to improve access to healthcare for underserved populations, advancing Sustainable Development Goal 10 (Reduced Inequalities). This study provides actionable recommendations for healthcare organizations and policymakers, emphasizing the need for secure, resilient, and sustainable digital ecosystems that enhance health outcomes while addressing global environmental and societal challenges.

Methodology for Safe and Secure AI in Diabetes Management.

The use of artificial intelligence (AI) in diabetes management is emerging as a promising solution to improve the monitoring and personalization of therapies. However, the integration of such technologies in the clinical setting poses significant challenges related to safety, security, and compliance with sensitive patient data, as well as the potential direct consequences on patient health. This article provides guidance for developers and researchers on identifying and addressing these safety, security, and compliance challenges in AI systems for diabetes management. We emphasize the role of explainable AI (xAI) systems as the foundational strategy for ensuring security and compliance, fostering user trust, and informed clinical decision-making which is paramount in diabetes care solutions. The article examines both the technical and regulatory dimensions essential for developing explainable applications in this field. Technically, we demonstrate how understanding the lifecycle phases of AI systems aids in constructing xAI frameworks while addressing security concerns and implementing risk mitigation strategies at each stage. In addition, from a regulatory perspective, we analyze key Governance, Risk, and Compliance (GRC) standards established by entities, such as the Food and Drug Administration (FDA), providing specific guidelines to ensure safety, efficacy, and ethical integrity in AI-enabled diabetes care applications. By addressing these interconnected aspects, this article aims to deliver actionable insights and methodologies for developing trustworthy AI-enabled diabetes care solutions while ensuring safety, efficacy, and compliance with ethical standards to enhance patient engagement and improve clinical outcomes.

Hybrid Ensemble Lightweight Cryptosystem for Internet of Medical Things Security

Internet of Medical Things (IoMT) is a fast-developing area that includes the use of connected medical devices to enhance patient care and expedite the procedures involved in the delivery of healthcare. Concerns about the safety and confidentiality of patient information are a roadblock to the broad use of telemedicine technologies like IoMT. Encryption is an essential part of IoMT security, and there is a wide variety of encryption methods that are used to safeguard sensitive patient data. This work implemented a hybrid ensemble lightweight cryptosystem (HELC) using probabilistic rivest cipher 6 (PRC6) encryption and modified feistel block cipher (MFBC) approaches. Initially, the data from users are applied to PRC6 encryption, which is symmetrical encryption and provides security at in abstract level. So, to provide more security to data, the MBFC is applied to PRC6 outcome. Then, the resultant data transferred over the IoMT environment to the destination. Finally, the MBFC decryption and PRC6 decryption operations are performed at receiver side, which resulted in decrypted outcome. The simulations results show that the proposed HELC consumed 0.0021 seconds of encryption time, and 0.000276 seconds of decryption time, which are lesser as compared to other approaches.

Cyber Security Strategies for Protecting Health Management Systems

The study examines advanced cybersecurity strategies for health management systems focusing on emerging technologies involving blockchain, artificial intelligence, and the security frameworks of Internet of Medical Things. Health management systems are more increasingly exposed to cybersecurity threats; hence, techniques for the protection of sensitive patient data are strengthened. In this present work, an effort has been made to assess how blockchain can be used effectively for storing data securely and thus achieve a very high penetration of reducing unauthorized access attempts at 97%. AI-driven threat detection systems achieved a 94% strength in the detection of health systems threats while traditional systems were at 12%. Adaptive key rotation for device authentication within IoT security framework resulted in a 92% success rate in guarding IoMT network against a data breach. Therefore, the convergence of these three technologies indicates an all-encompassing multilayered approach to security with enhanced resilience and integrity of data. This area needs constant innovation in light of the security issues to keep up with health management systems that maintain privacy and reliability. Sector-specific adaptations will drive further light on these technologies and provide more robust cybersecurity within the health sector.

Utilizing digital footprint analysis for end-to-end risk-based authentication in medical billing systems

Healthcare systems face escalating challenges in securing sensitive financial and patient data due to sophisticated fraud tactics and unauthorized access. This study presents a dynamic risk-based authentication (RBA) framework that leverages digital footprint analysis, including behavior monitoring, device recognition, and location-based anomaly detection, to strengthen security. The framework utilizes machine learning models, Isolation Forest for outlier detection, and recurrent neural networks for sequential behavior analysis, to assess risk in real-time, dynamically adjusting authentication requirements based on risk profiles. Privacy-preserving technologies such as homomorphic encryption and federated learning are integrated to comply with HIPAA and GDPR standards, ensuring secure data handling without centralization. Findings show that the proposed RBA framework effectively reduces false positives, improves detection accuracy, and provides a scalable solution for securing medical billing systems. This adaptive approach supports both user experience and stringent privacy compliance, laying the groundwork for more resilient healthcare data security systems. Future studies could extend this framework by incorporating blockchain to enhance data transparency and auditability across transactions.

Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions.

Chronic Kidney Disease (CKD) represents a significant global health challenge, contributing to increased morbidity and mortality rates. This review paper explores the current landscape of machine learning (ML) techniques employed in CKD prediction and diagnosis, highlighting recent trends, inherent challenges, innovative solutions, and future directions. Through an extensive literature survey, we identified key limitations and challenges, including the use of small datasets, the absence of stage-specific predictions, insufficient focus on model interpretability, and a lack of discussions on safeguarding patient privacy in managing sensitive CKD data. We considered these limitations and challenges as research gaps, and this review paper aims to address them. We emphasize the potential of Generative AI to augment dataset sizes, thereby enhancing model performance and reliability. To address the lack of stage-specific predictions, we highlight the need for effective multi-class models to accurately predict CKD stages, enabling tailored treatments and improved patient outcomes. Furthermore, we discuss the critical importance of model interpretability, utilizing methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to ensure transparency and trust among healthcare professionals. Privacy concerns surrounding sensitive patient data are also addressed. We present innovative privacy-preserving solutions using technologies, such as homomorphic encryption, federated learning, and blockchain. These solutions facilitate collaboration across institutions while maintaining patient confidentiality and addressing challenges related to limited generalizability and reproducibility in CKD prediction. This review informs healthcare professionals and researchers about advancements in ML for CKD prediction, to improve patient outcomes and address research gaps.

Automated anonymization of radiology reports: comparison of publicly available natural language processing and large language models.

Medical reports, governed by HIPAA regulations, contain personal health information (PHI), restricting secondary data use. Utilizing natural language processing (NLP) and large language models (LLM), we sought to employ publicly available methods to automatically anonymize PHI in free-text radiology reports. We compared two publicly available rule-based NLP models (spaCy; NLPac, accuracy-optimized; NLPsp, speed-optimized; iteratively improved on 400 free-text CT-reports (test set)) and one offline LLM approach (LLM-model, LLaMa-2, Meta-AI) for PHI-anonymization. The three models were tested on 100 randomly selected chest CT reports. Two investigators assessed the anonymization of occurring PHI entities and whether clinical information was removed. Subsequently, precision, recall, and F1 scores were calculated. NLPac and NLPsp successfully removed all instances of dates (n = 333), medical record numbers (MRN) (n = 6), and accession numbers (ACC) (n = 92). The LLM model removed all MRNs, 96% of ACCs, and 32% of dates. NLPac was most consistent with a perfect F1-score of 1.00, followed by NLPsp with lower precision (0.86) and F1-score (0.92) for dates. The LLM model had perfect precision for MRNs, ACCs, and dates but the lowest recall for ACC (0.96) and dates (0.52), corresponding F1 scores of 0.98 and 0.68, respectively. Names were removed completely or majorly (i.e., one first or family name non-anonymized) in 100% (NLPac), 72% (NLPsp), and 90% (LLM-model). Importantly, NLPac and NLPsp did not remove medical information, while the LLM model did in 10% (n = 10). Pre-trained NLP models can effectively anonymize free-text radiology reports, while anonymization with the LLM model is more prone to deleting medical information. Question This study compares NLP and locally hosted LLM techniques to ensure PHI anonymization without losing clinical information. Findings Pre-trained NLP models effectively anonymized radiology reports without removing clinical data, while a locally hosted LLM was less reliable, risking the loss of important information. Clinical relevance Fast, reliable, automated anonymization of PHI from radiology reports enables HIPAA-compliant secondary use, facilitating advanced applications like LLM-driven radiology analysis while ensuring ethical handling of sensitive patient data.

Advancements in health information technology and their role in enhancing cancer care: Innovations in early detection, treatment, and data privacy

The rapid advancement of Health Information Technology (HIT) has revolutionized cancer care, introducing new capabilities in early detection, individualized treatment, and data security. This paper reviews the key HIT innovations that have reshaped oncology, including the widespread adoption of Electronic Health Records (EHRs), the use of artificial intelligence (AI) in diagnostic processes, and the application of telemedicine and predictive analytics to improve treatment planning. These technologies have enabled earlier and more accurate diagnoses, expanded access to care, and facilitated data-driven, personalized treatment strategies. In parallel, this review addresses the importance of safeguarding sensitive patient data, focusing on encryption, anonymization techniques, and the regulatory frameworks like HIPAA and GDPR that ensure data privacy. Finally, the paper discusses future directions for HIT, particularly the potential of AI-driven decision support systems, real-time data analytics, and privacy-enhancing algorithms to further advance cancer care, creating a more secure, efficient, and patient-centered healthcare system.

Application of zero trust model in preventing medical errors.

Medical errors can occur in many areas of healthcare, including hospitals, clinics, and surgery centers. They can result in negative consequences for patients and their loved ones. Over the years, different methods have been used to reduce medical errors. Zero Trust is an information security model that denies access to applications and data by default. Other industries have successfully used Zero Trust Model (ZTM), and it has been shown to improve outcomes. This editorial analyzes how the ZTM can be introduced to prevent medical errors in healthcare settings. ZTM application in healthcare could potentially revolutionize patient safety by tightly controlling and monitoring access to sensitive patient data and critical systems. By enhancing security measures, the ZTM could address the paramount concerns of patient data privacy and safety in healthcare. The zero-trust approach offers a potential solution by identifying consistent causes of errors and providing viable solutions to prevent their recurrence. In the era of worsening ransomware attacks on healthcare systems, the ZTM could also have enormous implications in other cybersecurity aspects. With this manuscript, the authors advocate for the broader application of ZTM across other facets of healthcare cybersecurity.

The Critical Role of SREs in Safeguarding Healthcare Data Integrity and Availability

This article explores the critical role of Site Reliability Engineers (SREs) in the rapidly evolving landscape of healthcare technology. As healthcare organizations increasingly adopt digital solutions, SREs have become indispensable in ensuring data integrity, system availability, and regulatory compliance. The article discusses the unique challenges faced by SREs in healthcare environments, including managing vast amounts of sensitive patient data, implementing robust security measures, and maintaining high system reliability. It examines the key responsibilities of SREs, such as proactive monitoring, incident response, data consistency management, and security compliance. Furthermore, the article highlights the significant impact of SRE practices on healthcare delivery, including enhanced patient care, improved operational efficiency, risk mitigation, and cost reduction. Through a combination of statistical data and industry insights, this work underscores the growing importance of SREs in shaping the future of healthcare technology and improving overall patient outcomes.

Cybersecurity lessons from the Vastaamo psychotherapy data breach for psychiatrists and other mental healthcare providers.

The Vastaamo psychotherapy data breach in Finland is perhaps the largest cybersecurity incident in mental healthcare to date, resulting in significant patient harm. There are specific lessons for mental healthcare providers from an analysis of the incident. Case study of this specific electronic health record data breach, based on detailed media reporting. The issues raised include: the importance of governance of the cybersecurity of sensitive personal patient data, such as compliance with legislative requirements on privacy and data security; specific security measures such as de-identification of data, data protection via passwords, multi-factor authentication, firewalls and encryption; and timely and effective communication, and support of those who have been affected. The implications for mental healthcare providers, including psychiatrists and trainees, are that, within their capability, providers need to assess the efficacy and robustness of cybersecurity of electronic health record systems they use, and carefully consider the information that is recorded to minimise exposures such as in the Vastaamo breach.

Strengthening Healthcare Data Security with Ai-Powered Threat Detection

As the healthcare industry undergoes rapid digital transformation, the need for robust cybersecurity measures has never been more critical. AI-driven solutions, such as Machine Learning (ML) and anomaly detection, are proving to be pivotal in securing healthcare data. These technologies enable healthcare organizations to identify cyber threats proactively, automate incident response, and enhance data security. Furthermore, AI's ability to provide continuous network monitoring, predictive analytics, and real-time anomaly detection offers healthcare providers the tools needed to mitigate risks before they escalate. This research highlights the key applications of AI in healthcare data security, including its effectiveness in vulnerability management and regulatory compliance. It also delves into the ethical and operational challenges of integrating AI-driven threat detection systems into healthcare settings, including concerns related to bias in AI models, regulatory hurdles, and the complexity of AI system integration into existing healthcare infrastructures. In a 2023 study, Accenture reported that AI-based cybersecurity systems reduced detection and response time by up to 60%, illustrating how AI accelerates response to potential data breaches​. Similarly, HIMSS noted that the risk of data breaches in healthcare could be halved by AI technologies that continuously monitor and analyze data​. These findings emphasize AI's role in minimizing the delays and errors typically associated with human-driven responses​. AI techniques such as supervised, unsupervised, and semi-supervised learning play a crucial role in anomaly detection. These models identify irregular patterns in healthcare systems, flagging potential threats even when subtle​. For example, deep learning approaches, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can uncover outliers in healthcare data, enhancing system security​. The ability of AI to continuously learn from previous attacks ensures that systems evolve with emerging threats, further solidifying its place in healthcare cybersecurity​. Additionally, AI aids in predictive analytics, which can forecast future cyber threats based on historical data, allowing healthcare providers to address vulnerabilities proactively​. By leveraging such capabilities, AI not only safeguards sensitive patient data but also enables healthcare institutions to stay compliant with stringent regulatory standards like HIPAA and GDPR​. However, AI implementation in healthcare security comes with its own challenges. Integrating AI into legacy systems requires substantial infrastructure upgrades, and the complexity of dynamic encryption can strain system resources​. Furthermore, the ethical concerns surrounding AI, such as the transparency of decision-making processes and potential bias in AI algorithms, must be carefully managed to ensure equitable and effective security solutions​

Machine learning models for personalised healthcare on marketable generative-AI with ethical implications

Personalised healthcare, underpinned by a deep understanding of individual patient variability, demands innovative solutions. Machine Learning (ML) models offer a promising avenue to achieve this by facilitating the development of enhanced Digital Twins (DTs). This research proposes a novel framework for creating DTs tailored to individual patients, considering not only physical attributes but also the intricate interplay of social, and biological factors. By capturing this comprehensive patient profile, ML-powered DTs have the potential to revolutionise healthcare by enabling predictive, preventive, and personalised care strategies, ultimately leading to improved patient outcomes and the development of a marketable, trust-worthy product. Current healthcare solutions lack personalisation, often failing to consider individual differences in disease presentation and response to treatment. Traditional DTs in healthcare often adopt a disease-centric or organ-specific approach, thereby restricting their capacity to deliver comprehensive, personalised care. To address this limitation, we propose a holistic Artificial Intelligence (AI) framework centered on ML models. Initially focusing on diabetes, our research aims to enhance diagnosis, treatment, and predictive capabilities through personalised insights, thereby optimising patient outcomes and care management. The benefits identified with our ML model are early disease prevention and risk stratification, optimised treatment planning and therapy selection, enhanced patient-physician communication and shared decision-making, reduced healthcare costs and improved resource allocation. Our models are designed to optimise patient care while prioritising safety and societal benefit. To ensure this, we have conducted a thorough assessment of potential ethical implications. Key challenges identified include data privacy and security, algorithmic bias, diagnostic accuracy, data interoperability and standardisation, integration with existing healthcare systems, ethical management of sensitive patient data, refinement of ML methodologies, addressing legal and ethical AI challenges, and suggesting robust ethical guidelines. A comprehensive evaluation of accuracy, reliability, and associated risks will be conducted prior to full-scale integration into the healthcare ecosystem to establish a robust ethical framework for our research models.

Machine Learning-Driven Assessment and Security Enhancement for Electronic Health Record Systems

The digitalized patient-centric system, the Electronic Health Record (EHR), is a platform where comprehensive health information is stored, managed, and accessed electronically. The primary findings of this study aim to secure sensitive patient data and increase overall system resilience by demonstrating that machine learning can evaluate vulnerabilities and improve the security of Electronic Health Record (EHR) systems. This research examines the prospects of incorporating machine learning-driven assessment tools and safety improvements in EHRs to enhance data protection in the healthcare industry. The proposed method utilizes the implementation of machine learning classifiers, specifically the XGBoost and LightGBM models. These classifiers are employed to enhance various aspects of the system, such as data protection and security, within the framework of EHRs. The study emphasizes the efficiency of these machine learning classifiers in ensuring that EHR systems are secure enough to deal with any problem that may occur due to threats posed by external factors or hackers. The findings reveal that the XGBoost model always has outstanding performance, with a near-perfect Receiver Operating Characteristic Curve (ROC) having an AUC equal to 1.00, indicating close to perfect accuracy in distinguishing positive from negative cases. Similarly, LightGBM has a perfect ROC curve as well. Therefore, its performance would be considered flawless. Consequently, future developments could lead to sophisticated machine learning models besides those that have already been developed. Improving data storage through encryption and building safer communication protocols should also be considered to make these systems withstand new security problems. Thus, this study contributes to the existing literature on applying technology to safeguard vulnerable medical records while fostering a safe and efficient healthcare ecosystem.

Using the Ethereum Blockchain in the Internet of Things Network for IT Diagnostics

The article discusses the use of Ethereum blockchain technology in the Internet of Things (IoT) network for IT diagnostics of patients, which increases data security and user privacy. This integration is proving effective for storing and managing sensitive data of patients with neurological diseases. An integrated system architecture has been developed that combines the IoT network, the IPFS (InterPlanetary File System) file structure with the Ethereum blockchain to create a reliable data storage model. This system ensures efficient, secure and transparent data processing, optimizing the processes of data registration, authorization and verification. Using IPFS for decentralized file storage, along with the Ethereum blockchain to create tamper-proof medical records, provides increased efficiency, scalability and privacy. During the experiments, the process of creating and testing the system was implemented, including setting up the environment, connecting an IPFS node, programming Ethereum smart contracts, sampling voice data and storing their hashes.

A Secure Authenticated Healthcare Data Analysis Mechanism in IoMT‐Enabled Healthcare

ABSTRACTWe frequently wait long when we go to the hospital for a doctor's appointment. However, many people cannot afford to stay in such lengthy lines in today's busy world. Daily, we observe that manually performing such tasks takes considerable time, and automating such processes can save time. Securing the information the user provides, medical staff, and devices is crucial. Before accessing such information, the user must be authenticated to ensure that unauthorized persons cannot retrieve patient records. Internet‐of‐Things (IoT) has revolutionized the healthcare industry with the emergence of IoT‐based healthcare systems, called Internet‐of‐Medical‐Things (IoMT). It offers efficient and personalized patient care. However, the vast amounts of sensitive patient data transmitted and stored within these systems raise serious concerns about data security and privacy. This article proposes a secure healthcare data analysis mechanism with user sign‐in authentication capability for the IoMT‐enabled healthcare system (SA‐IoMTH). It addresses the unique security challenges faced by these systems. The formal security verification using the scyther tool and a security analysis of SA‐IoMTH are provided, proving its security against various possible attacks. The proposed SA‐IoMTH outperforms the other existing approaches. Finally, a smartphone application is also implemented for SA‐IoMTH to check its usefulness in real‐world scenarios.

Security of End-to-End medical images encryption system using trained deep learning encryption and decryption network

The Internet of Medical Things (IoMT) links medical devices and wearable, enhancing healthcare. To secure sensitive patient data over the IoMT, encryption is vital to retain confidentiality, prevent tampering, ensure authenticity, and secure data transfer. The intricate neural network architecture of deep learning models adds a layer of complexity and non-linearity to the encryption process, rendering it highly resistant to plaintext attacks. Specifically, the Cycle_GAN network is used as the leading learning network. This work suggests deep learning-based encryption for medical images using Cycle_GAN, a Generative Adversarial Network. Cycle_GAN changes images without paired training data that improves quality and feature preservation. Unlike conventional image-to-image translation techniques, Cycle_GAN doesn’t require a dataset with corresponding input–output pairs. Traditional methods typically needs paired data to learn the mapping between input and output images. Paired data can be challenging to obtain, specifically in medical imaging where gathering and annotating data can be time-consuming, laborious and expensive. The use of Cycle_GAN overwhelms this constraint by using unpaired data, where the input and output images are not explicitly paired. This method ensures confidentiality, authenticity, and secure transfer. Cycle_GAN consists of two major components: a generator used to modify the images, and a discriminator used to distinguish between real and fake images. Further, the Binary-Cross Entropy loss function is employed to train the network for precise predictions. The experiments are carried out on skin cancer datasets. The results demonstrate high-level efficient, systematic and coherent encryption as compared with other modernized medical image encryption methods. The proposed technique offers several benefits, including efficient encryption and decryption and robustness against unauthorized access.

Real-time privacy-preserved auditing for shared outsourced data

Health providers need to share patient information across healthcare networks efficiently and securely to improve medical and health services. Timely data synchronization among relevant parties is crucial for effectively containing and preventing the worsening of the condition. However, ensuring rapid information sharing while maintaining the security of sensitive patient data remains a pressing concern. In this paper, we introduce a cloud storage integrity auditing scheme that can protect auditors from procrastinating and preserve the privacy of sensitive information. Our proposed system requires healthcare institutions to encrypt sensitive patient data before uploading it to the cloud. It mandates the use of a data sanitizer for the secure processing of encrypted data blocks. Auditors must verify data integrity and promptly submit their audit results to the blockchain within a predefined time frame. Leveraging the time-sensitive nature of blockchain technology, healthcare institutions can monitor auditor compliance within the allotted validation timeframe. We conducted comprehensive security analysis and performance evaluations to demonstrate the feasibility and effectiveness of our solution in addressing the challenges of secure and timely cloud storage in healthcare settings.

A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT

AbstractInternet of Medical Things have vastly increased the potential for remote patient monitoring, data‐driven care, and networked healthcare delivery. However, the connectedness lays sensitive patient data and fragile medical devices open to security threats that need robust intrusion detection solutions within cloud‐edge services. Current approaches need modification to be able to handle the practical challenges that result from problems with data quality. This paper presents a hybrid intrusion detection framework that enhances the security of IoMT networks. There are three modules in the design. First, an XGBoost‐based noise detection model is used to identify data anomalies. Second, adaptive resampling with ADASYN is done to fine‐tune the class distribution to address class imbalance. Third, ensemble learning performs intrusion detection through a Random Forest classifier. This stacked model coordinates techniques that filter noise and preprocess imbalanced data, identifying threats with high accuracy and reliability. These results are then experimentally validated on the UNSW‐NB15 benchmark to demonstrate effective detection under realistically noisy conditions. The novel contributions of the work are a new hybrid structural paradigm coupled with integrated noise filtering, and ensemble learning. The proposed advanced oversampling with ADASYN gives a performance that surpasses all others with a reported 92.23% accuracy.