Hospital logistics can significantly benefit from autonomous robotic technologies capable of alleviating personnel workload, reducing operational costs, and streamlining internal workflows. This paper introduces HOSBOT (HOSpital roBOT), a modular and cost-effective robotic system designed to enhance hospital logistics and workflow management through autonomous missions and real-time monitoring of transported items. HOSBOT combines a commercially available autonomous mobile robot with a customisable non-motorised cart, the SmartRack, which accommodates standalone, sensorised SmartBoxes equipped with Radio Frequency IDentification technology. This paper describes the overall design of HOSBOT, including its mechanical, electrical, and software interfaces. The system has been validated in three pilot experiments in real hospital environments across Europe, demonstrating good levels of acceptability, usability and efficacy. This innovative robotic solution highlights the potential for scalable integration of autonomous mobile robots in healthcare and other indoor logistics settings.

Monitoring of advanced cancer patients' health, treatment, and supportive care is essential for improving cancer survival outcomes. Traditionally, oncology has relied on clinical metrics such as survival rates, time to disease progression, and clinician-assessed toxicities. In recent years, patient-reported outcome measures (PROMs) have provided a complementary perspective, offering insights into patients' health-related quality of life (HRQoL). However, collecting PROMs consistently requires frequent clinical assessments, creating important logistical challenges. Wearable devices combined with artificial intelligence (AI) present an innovative solution for continuous, real-time HRQoL monitoring. While deep learning models effectively capture temporal patterns in physiological data, most existing approaches are unimodal, limiting their ability to address patient heterogeneity and complexity. This study introduces a multimodal deep learning approach to estimate HRQoL in advanced cancer patients. Physiological data, such as heart rate and sleep quality collected via wearable devices, are analyzed using a hybrid model combining convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks with an attention mechanism. The BiLSTM extracts temporal dynamics, while the attention mechanism highlights key features, and CNNs detect localized patterns. PROMs, including the Hospital Anxiety and Depression Scale (HADS) and the Integrated Palliative Care Outcome Scale (IPOS), are processed through a parallel neural network before being integrated into the physiological data pipeline. The proposed model was validated with data from 204 patients over 42 days, achieving a mean absolute percentage error (MAPE) of 0.24 in HRQoL prediction. These results demonstrate the potential of combining wearable data and PROMs to improve advanced cancer care.

Gut microbiome research has made tremendous progress, especially with the integration of machine learning and artificial intelligence that can provide new insights from complex microbiome data and its impact on human health. The use of explainable artificial intelligence is becoming critical in medicine and adopting it in precision medicine-models leveraging gut microbiome data is appealing for providing more transparency and trustworthiness in clinical research. This scoping review evaluates the use of machine learning and explainable artificial intelligence techniques and identifies existing gaps in knowledge in this research area to suggest future research directions. Online databases (PubMed and Scopus) were searched to retrieve papers published between 2018-2024, and from which we selected 76 publications. Different clinical applications of machine learning and artificial intelligence techniques in gut microbiome studies were explored in the reviewed articles. We observed a high prevalence in the use of black box models in the field, with Random Forest being the most used algorithm. The explainability remains somewhat limited in the field, but it appears to be improving. Researchers showed interest in SHAP applications as an explainable technique. Finally, not enough attention was paid to the reproducibility of the research work published. This review highlights opportunities for advancing research on explainable artificial intelligence models in the field of microbiome, supporting future applications of microbiome-based precision medicine.

Synthetic data generation is an emerging solution to address current data privacy and availability challenges, especially in healthcare applications and wearable devices. This study aims to generate synthetic multivariate time-series data that simulates physical activities, preserving the variability and dependencies observed in real-world data (RWD). An innovative approach that includes data organization, preprocessing, and model training on a dataset of 100 users with several datetime, numeric, and categorical variables is conducted. By using two artificial intelligence (AI) models — Periodic Autoregressive (PAR) and Conditional Tabular Generative Adversarial Network (CTGAN)—a total of 100 new synthetic users with temporal and feature-specific activity patterns were generated. Evaluation results showed strong alignment between synthetic and original data, with mean values of 0.014 for Kolmogorov-Smirnov (KS) Test, 0.057 for Jensen-Shannon (JS) Distance, and 0.011 for Pairwise Correlation, indicating realistic data relationships and feature distributions. Despite these preliminary results, future work includes enhancing computational efficiency and scalability, expanding its generalizability across diverse datasets, and further validation.

Effective diabetes management is crucial to pre-venting serious complications in patients. This study employs machine learning techniques to analyze data from 11 Spanish hospital emergency departments, with the goal of improving the quality of life for individuals with type I and type II diabetes. Our model demonstrates a prediction accuracy of up to 95% for hypo- and hyperglycemia, and highlights significant insights into antidiabetic treatments. The results were validated using a decision tree and correlation plots, showing relationships between key variables. This approach shows potential for large-scale prediction of glycemic events and provides valuable support for clinical decision-making.

As diabetes management becomes more complicated, there is an increasing interest in understanding how to manage diabetes with physical activity. Our study aimed to investigate the role of wearable, non-invasive technologies in collecting data related to physical activity to model them via artificial intelligence methods for efficient diabetes management. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (also known as PRISMA) protocol and searched three databases, namely PubMed, Scopus, and Web of Science. Out of 960 titles, we included 32 in the full-text analysis. Results showed two main methods were used for the analysis, i.e., statistical and classification modeling. Results indicate among the employed regression methods, linear regression was used more than other methods, and the most common classification-based method for analyzing data was the Artificial Neural Network method. Assessing the quality of papers that used the classification method was done through Prediction model Risk Of Bias Assessment Tool (also known as PROBAST) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (also known as TRIPOD) tools. Based on PROBAST outcomes, although the risk of bias was low in most of the works, explaining the analyzing method specifically, the method of handling missing data needs more attention. Upon evaluating papers using the TRIPOD, it realized that there is a need to place emphasis on improving the quality of the presentation and explanation of the result. According to our review, the conjunction of non-invasive technologies and artificial intelligence is promising in managing diabetic risk factors for real-time monitoring of physical activities, enabling regular clinical intervention and optimized medical treatment.

The average life expectancy of the world’s population is increasing and the healthcare systems sooner than later will be compromised by its reduced capacity and its highly economic cost; in addition, the age distribution of the population is leading towards the older spectrum. This trend will lead to immeasurable and unexpected economic problems and social changes. In order to face up this challenge and complex economic and social problem, it is necessary to rely on the appropriate digital tools and technological infrastructures for ensuring that the elderly are properly cared in their everyday living environments and they can live independently for longer. This article presents ACTIVAGE IoT Ecosystem Suite (AIoTES), a concrete reference architecture and its implementation process that addresses these issues and that was designed within the first European Large Scale Pilot, ACTIVAGE, a H2020 funded project by the European Commission with the objective of creating sustainable ecosystems for Active and Healthy Ageing (AHA) based on Internet of Things and big data technologies. AIoTES offers platform level semantic interoperability, with security and privacy, as well as Big Data and Ecosystem tools. AIoTES enables and promotes the creation, exchange and adoption of cross-platform services and applications for AHA. The number of existing AHA services and solutions are quite large, especially when state-of-the-art technology is introduced, however a concrete architecture such as AIoTES gains more importance and relevance by providing a vision for establishing a complete ecosystem, that looks for supporting a larger variety of AHA services, rather than claiming to be a unique solution for all the AHA domain problems. AIoTES has been successfully validated by testing all of its components, individually, integrated, and in real-world environments with 4345 direct users. Each validation is contextualized in 11 Deployment Sites (DS) with 13 Validation Scenarios covering the heterogeneity of the AHA-IoT needs. These results also show a clear path for improvement, as well as the importance for standardization efforts in the ever-evolving AHA-IoT domain.

Head and Neck Cancer is the seventh cancer in incidence worldwide and this high mortality is due to the major cases are diagnosed in advanced stages. Currently, the selection of treatment is based on the Tumor-lymph-Nodes-Metastasis prognostic system. This system only considers a few risk factors, being inadequate due to the heterogeneity of such tumors. Within BD2Decide project, an Integrated Decision Support System is being implemented to link data coming from different disciplines with the purpose of providing the necessary information to tailor treatment and care delivery pathways to each Head and Neck Cancer patient. A clinical study with more than 1000 of patients is used to validate the system.

Objective: To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods: The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results: The use of the decision support component in clinical activities produced a reduction in visit duration (P ≪ .01) and an increase in the number of screening exams for complications (P < .01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system's capability of identifying and understanding the characteristics of patient subgroups treated at the center. Conclusion: Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.

Existing initiatives to improve physical, mental, and social condition of senior citizens, which in Europe fall under the name of Active and Healthy Ageing, are including technological paradigms as main driver for innovation uptake. Among these paradigms, Smart Cities and the Internet of Things are of utmost importance. However, these initiatives may benefit from unified visions, efforts, and frameworks when it comes to defining technological solutions that take the most of both paradigms. We have defined an iterative approach, which combines user centred design techniques, technological development approaches, and a multifaceted adaptation process, to define a solution for Active and Healthy Ageing that makes use of the two paradigms. The solution is being defined in the context of two research and innovation projects, City4Age and ACTIVAGE, during which a solution is going to be defined and evaluated in the city of Madrid. Results show how Smart Cities and Internet of Things contribute to the solution, from a user (user needs and use cases) and a service delivery (technologies, architecture, and suppliers) perspective. In conclusion, we find the cocreation framework extremely useful for the Active and Health Ageing domain, and the proposed implementation of it is functioning, although there is room for improvement.

The availability of new tools able to support patient monitoring and personalized care may substantially improve the quality of chronic disease management. A personalized healthcare pathway (PHP) has been developed for diabetes disease management and integrated into an information and communication technology system to accomplish a shift from organization-centered care to patient-centered care. A small-scale exploratory study was conducted to test the platform. Preliminary results are presented that shed light on how the PHP influences system usage and performance outcomes.