The convergence of edge intelligence and networked medical infrastructures in the Artificial Intelligence of Medical Things (AIoMT) is transforming healthcare toward personalization and predictive intervention. In this paradigm, high-resolution physiological data continuously flow between wearable or implantable devices, edge nodes, and cloud analytics platforms. Such connectivity enables advanced diagnostic modeling and real-time decision support. However, it also enlarges the attack surface. AIoMT components are exposed to impersonation, replay, and man-in-the-middle attacks. Therefore, secure data exchange becomes essential. Authentication and key exchange (AKE) schemes address this requirement by enabling mutual authentication and session key establishment over insecure channels. Nevertheless, many existing centralized designs suffer from single points of failure and insider threats. Several blockchain-assisted approaches still retain centralized identity traceability. In addition, most AKE schemes either neglect physical security, lack tolerance to intrinsic physical unclonable function (PUF) noise, or store sensitive PUF challenge–response pairs, which increases the risk of modeling attacks. To address these issues, we propose a fully decentralized authentication and key exchange scheme (FDAKES) for AIoMT. FDAKES adopts a$(t,n)$threshold-based root of trust across multiple registration centers (MRCs) to remove unilateral control in registration and tracing. Its server-independent AKE process combines threshold-protected identities, dynamic nonces, timestamps, and PUF and biometric enhanced credentials to achieve perfect forward secrecy. Decentralized conditional traceability preserves user anonymity while allowing identity recovery only with unanimous MRCs consent. By integrating PUF with a fuzzy extractor, FDAKES enables stable secret regeneration without storing raw challenge–response pairs, thereby mitigating modeling threats. We formally prove protocol correctness for login authentication and mutual key agreement. We further establish semantic security of the session key under the real-or-random model, showing that the adversary advantage is negligible in the random oracle model. An extensive informal analysis demonstrates resistance to impersonation, replay, guessing, modeling, physical, man-in-the-middle, and key compromise attacks. Experimental evaluation demonstrates that FDAKES reduces total computational overhead by up to 40.95% and at least 17.25% percent compared with recent state-of-the-art AKE schemes, while communication cost is reduced by up to 76.73% and at least 5% across representative baselines. This work establishes a robust and fully decentralized trust foundation for next-generation smart healthcare systems.

Accurate sleep staging is vital for evaluating sleep quality and diagnosing sleep disorders. Yet most automated sleep staging methods rely on large datasets labeled by experts. However, clinical annotation is both time-consuming and subjective, making it difficult to obtain sufficient high-quality data for automated sleep staging research. To address this bottleneck, we propose a few-shot, dual-branch contrastive pre-training framework for single-channel electroencephalogram (EEG)-based sleep staging. The framework first conducts fully self-supervised pre-training on unlabeled data, then performs fine-tuning that requires only a small set of labeled samples. We developed and evaluated our solution with the public Sleep-EDF-v2 EEG dataset, achieving state-of-the-art results despite using limited labeled data. Specifically, with only 1% labeled data, our method delivers an accuracy of 76.10% and Macro F1-score of 61.34%, comparable to supervised models trained on 100% labeled data. We further validated our approach on the ISRUC-1 and ISRUC-3 datasets, where similar robust results were consistently observed. The ability to effectively develop sleep classification models using minimal labeled data demonstrates the potential value of our framework across diverse clinical settings.

Cardiovascular disease (CVD) remains one of the leading global health challenges, calling for accurate and scalable detection methods. This work presents MoViE, a multimodal fusion model based on sparsely activated experts, designed to effectively fuse electrocardiogram (ECG) and electronic health record (EHR) data for CVD detection. MoViE adopts a dual-branch architecture. In the ECG branch, the feed-forward layers of the Vision Transformer (ViT) are replaced with a Mixture-Of-Experts (MoE) module that incorporates top-k routing and shared experts, enabling the model to capture both fine-grained waveform features and long-range temporal dependencies. The EHR branch encodes structured clinical records using TF-IDF, followed by a multilayer perceptron (MLP) to extract semantic representations. To support modality interaction, a MoE-based fusion module is introduced, where a gating network adaptively selects experts to combine complementary features. Extensive experiments on the MIMIC-IV-ECG dataset for myocardial infarction detection demonstrate that MoViE outperforms existing unimodal and multimodal mainstream baselines, achieving 87.98% Accuracy, 90.70% AUC, 66.15% F1-score, and 59.12% MCC, highlighting the potential of MoViE as a general framework for intelligent disease detection.

Continuous blood pressure (BP) monitoring is crucial for cardiovascular care but is often constrained by financial concerns. Continuous BP estimation based on ballistocardiogram (BCG) signals offers a non-invasive and cost-effective alternative. Traditional approaches to BP estimation from BCG signals have been hampered by single-channel noise interference, high costs of multi-signal acquisition, and insufficient feature capture. This paper introduces BP-STFNet, a novel deep learning framework that transcends these limitations by leveraging a unique fusion of time-frequency domain information. BP-STFNet enhances input signal quality and ensures robustness against physical movement artifacts. Our custom-designed parallel gated dilated convolution architecture, along with Squeeze-and-Excitation ResNet and Bidirectional Gated Recurrent Unit (Bi-GRU) module, extract detailed features and capture dynamic temporal patterns. The framework achieves a Mean Absolute Error (MAE) of 4.08 mmHg for systolic and 2.12 mmHg for diastolic pressure measurements, demonstrating highly competitive results for systolic pressure estimation and state-of-the-art performance for diastolic pressure prediction. The promising results achieved with BP-STFNet suggest its potential as a viable solution for both continuous and real-time BP monitoring, paving the way for improved cardiovascular health management.

BACKGROUND: An inherent difference exists between male and female bodies, the historical under-representation of females in clinical trials widened this gap in existing healthcare data. The fairness of clinical decision-support tools is at risk when developed based on biased data. This paper aims to quantitatively assess the gender bias in risk prediction models. We aim to generalize our findings by performing this investigation on multiple use cases at different hospitals. METHODS: First, we conduct a thorough analysis of the source data to find gender-based disparities. Secondly, we assess the model performance on different gender groups at different hospitals and on different use cases. Performance evaluation is quantified using the area under the receiver-operating characteristic curve (AUROC). Lastly, we investigate the clinical implications of these biases by analyzing the underdiagnosis and overdiagnosis rate, and the decision curve analysis (DCA). We also investigate the influence of model calibration on mitigating gender-related disparities in decision-making processes. RESULTS: Our data analysis reveals notable variations in incidence rates, AUROC, and over-diagnosis rates across different genders, hospitals and clinical use cases. However, it is also observed the underdiagnosis rate is consistently higher in the female population. In general, the female population exhibits lower incidence rates and the models perform worse when applied to this group. Furthermore, the decision curve analysis demonstrates there is no statistically significant difference between the model's clinical utility across gender groups within the interested range of thresholds. CONCLUSION: The presence of gender bias within risk prediction models varies across different clinical use cases and healthcare institutions. Although inherent difference is observed between male and female populations at the data source level, this variance does not affect the parity of clinical utility. In conclusion, the evaluations conducted in this study highlight the significance of continuous monitoring of gender-based disparities in various perspectives for clinical risk prediction models.

OBJECTIVE: Machine learning (ML) algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few efforts have been made in extending the developed solutions to other events or to different hospitals. We provide a scalable solution to extend the process of clinical risk prediction model development of multiple diseases and their deployment in different Electronic Health Records (EHR) systems. MATERIALS AND METHODS: We defined a generic process for clinical risk prediction model development. A calibration tool has been created to automate the model generation process. We applied the model calibration process at four hospitals, and generated risk prediction models for delirium, sepsis and acute kidney injury (AKI) respectively at each of these hospitals. RESULTS: The delirium risk prediction models have on average an area under the receiver-operating characteristic curve (AUROC) of 0.82 at admission and 0.95 at discharge on the test datasets of the four hospitals. The sepsis models have on average an AUROC of 0.88 and 0.95, and the AKI models have on average an AUROC of 0.85 and 0.92, at the day of admission and discharge respectively. DISCUSSION: The scalability discussed in this paper is based on building common data representations (syntactic interoperability) between EHRs stored in different hospitals. Semantic interoperability, a more challenging requirement that different EHRs share the same meaning of data, e.g. a same lab coding system, is not mandated with our approach. CONCLUSIONS: Our study describes a method to develop and deploy clinical risk prediction models in a scalable way. We demonstrate its feasibility by developing risk prediction models for three diseases across four hospitals.

The population of elderly people is increasing rapidly, which becomes a predominant aspect of our society. For several reasons so significant a share of the human society is simply regarded as "retired" -- a word condemning the elderly to a reduced participation in all active life, regardless of their actual conditions and abilities. In previous work, we discussed how community resources can be organized in a better way. In particular we introduced a so-called mutual assistance community -- a digital ecosystem that removes any predefined and artificial distinction between care-givers and care-takers and provides a service-oriented infrastructure for intelligent matching of the supply and demand of services. According to this new paradigm all people are potentially active participants to activities defined by the people's current needs, abilities, locations, and availabilities. Moving from this conceptual view to practical implementation calls for an architecture able to match adequately demand and supply of services. This paper presents an implementation of such an architecture based on semantic service description and matching. In comparison with our previous implementation, main added values include a greater flexibility in service representation and service matching and considerable improvements in performance.

Elder people are becoming a predominant aspect of our societies. As such, solutions both efficacious and cost-effective need to be sought. This paper proposes a design to construct a virtual ambient assisted living community where dwellers make contributions to the community so as to best utilize resources and minimize costs. We use service oriented architecture (SOA) to orchestrate the available resources inside the community, thus bringing social intelligence to the social computing. We also propose building such a virtual community making use of virtual reality and in the form of serious game [Stuart Rory, 1996]. Daily activities and instruments (such as sensors, cameras, etc.) in real-life may be translated into their virtual community equivalents, and activities happening in the virtual world will trigger corresponding actions in the real world, so that inter-reality may be obtained through this virtual community. We expect such a virtual community could help not only efficiently utilizing the social resources in maintaining the independent living of the elderly people, but also helping these people maintain their connections to the society and bring them entertainment, so that the quality of their living standard may be improved at the same time.

Elder people are becoming a predominant aspect of our societies. As such, solutions both efficacious and cost-effective need to be sought. The approach pursued so far to solve this problem used to increase the number of people working in the health sector, e.g. doctors, nurses, etc. This increases the costs, which is becoming a big burden for countries. In this paper we propose a new concept in the health management of elder people, which we name as "participant". We propose the "participant" concept to encourage elder people to participate in those group activities that they are able to. Their roles in these activities are not passively requesting help, but actively participating to some healthcare processes. Characteristics of the participant approach are that medical resources are efficiently spared with this model, and the social network of the elder people is kept. A "virtual community" for mutual assistance is set up in this paper, and the simulations demonstrate that the "participant" model could fully utilize the community resources. Furthermore, the psychological health of the elder people will be improved.

Elder people are becoming a predominant aspect of our societies and solutions both efficacious and cost-effective need to be sought. This paper proposes a service-oriented infrastructure approach to this problem. We propose an open and integrated service infrastructure to orchestrate the available resources (smart devices, professional carers, informal carers) to help elder or disabled people. Main characteristic of our design is the explicitly support of dynamically available service providers such as informal carers. By modeling the service description as Semantic Web Services, the service request can automatically be discovered, reasoned about and mapped onto the pool of heterogeneous service providers. We expect our approach to be able to efficiently utilize the available service resources, enrich the service options, and best match the requirements of the requesters.