The prevalence of diet-related diseases underscores the imperative for innovative management approaches. The deployment of smart solutions signifies a paradigmatic evolution, capitalising on advanced technologies to enhance precision and efficacy. This paper aims to present and explore smart solutions for the management of diet-related diseases, focusing on leveraging advanced technologies, such as connected care, the Internet of Medical Things (IoMT), and remote health monitoring systems (RHMS), to address the rising prevalence of diet-related diseases. This transformative approach is exemplified in case studies focusing on tailored RHMS capabilities. This paper aims to showcase the potential of three RHMS in introducing a novel evaluation method and their customisation for proactive management of conditions influenced by dietary habits. The RO-SmartAgeing System uniquely addresses age-related aspects, providing an integrated approach that considers the long-term impact of dietary choices on ageing, marking an advanced perspective in healthcare. The NeuroPredict Platform, leveraging complex neuroinformatics, enhances the understanding of connections between brain health, nutrition, and overall well-being, contributing novel insights to healthcare assessments. Focused on liver health monitoring, the HepatoConect system delivers real-time data for personalized dietary recommendations, offering a distinctive approach to disease management. By integrating cutting-edge technologies, these smart solutions transcend traditional healthcare boundaries.

Breast cancer is a serious medical condition that requires early detection for successful treatment. Mammography is a commonly used imaging technique for breast cancer screening, but its analysis can be time-consuming and subjective. This study explores the use of deep learning-based methods for mammogram analysis, with a focus on improving the performance of the analysis process. The study is focused on applying different computer vision models, with both CNN and ViT architectures, on a publicly available dataset. The innovative approach is represented by the data augmentation technique based on synthetic images, which are generated to improve the performance of the models. The results of the study demonstrate the importance of data pre-processing and augmentation techniques for achieving high classification performance. Additionally, the study utilizes explainable AI techniques, such as class activation maps and centered bounding boxes, to better understand the models’ decision-making process.

Mild cognitive impairment (MCI) may occur with old age and is associated with increased cognitive deterioration compared to what is normal. This may affect the person’s quality of life, health, and independence. In this ageing worldwide context, early diagnosis and personalized assistance for MCI therefore become crucial. This paper makes two important contributions: (1) a system (RO-SmartAgeing) to address MCI, which was developed for Romania; and (2) a set of criteria for evaluating its impact on remote health monitoring. The system aims to provide customized non-invasive remote monitoring, health assessment, and assistance for the elderly within a smart environment set up in their homes. Moreover, it includes multivariate AI-based predictive models that can detect the onset of MCI and its development towards dementia. It was built iteratively, following literature reviews and consultations with health specialists, and it is currently being tested in a simulated home environment. While its main strength is the potential to detect MCI early and follow its evolution, RO-SmartAgeing also supports elderly people in living independently, and it is safe, comfortable, low cost, and privacy protected. Moreover, it can be used by healthcare institutions to continuously monitor a patient’s vital signs, position, and activities, and to deliver reminders and alarms.

Schizophrenia is a complex neuropsychiatric disorder characterized by disruptions in brain connectivity and cognitive functioning. Continuous monitoring of neural activity is essential, as it allows for the detection of subtle changes in brain connectivity patterns, which could provide early warnings of cognitive decline or symptom exacerbation, ultimately facilitating timely therapeutic interventions. This paper proposes a novel approach for detecting schizophrenia-related abnormalities using deep learning (DL) techniques applied to electroencephalogram (EEG) data. Using an openly available EEG dataset on schizophrenia, the focus is on preprocessed event-related potentials (ERPs) from key electrode sites and applied transfer entropy (TE) analysis to quantify the directional flow of information between brain regions. TE matrices were generated to capture neural connectivity patterns, which were then used as input for a hybrid DL model, combining convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The model achieved a performant accuracy of 99.94% in classifying schizophrenia-related abnormalities, demonstrating its potential for real-time mental health monitoring. The generated TE matrices revealed significant differences in connectivity between the two groups, particularly in frontal and central brain regions, which are critical for cognitive processing. These findings were further validated by correlating the results with EEG data obtained from the Muse 2 headband, emphasizing the potential for portable, non-invasive monitoring of schizophrenia in real-world settings. The final model, integrated into the NeuroPredict platform, offers a scalable solution for continuous mental health monitoring. By incorporating EEG data, heart rate, sleep patterns, and environmental metrics, NeuroPredict facilitates early detection and personalized interventions for schizophrenia patients.

The COVID-19 pandemic amplified the elderly’s aging-related dysfunctionalities and vulnerabilities. Research surveys, aimed at evaluating the socio–physical–emotional state of the elderly and obtaining data on their access to medical services and information media services during the pandemic, were carried out on Romanian respondents aged 65+. Identification and mitigation of the risk of emotional and mental long-term decline of the elderly after SARS-CoV-2 infection, based on the implementation of a specific procedure, can be performed through Remote Monitoring Digital Solutions (RMDSs). The aim of this paper is to propose a procedure for the identification and mitigation of the risk of emotional and mental long-term decline of the elderly after SARS-CoV-2 infection that comprises RMDS. The importance of using the knowledge obtained by COVID-19-related surveys corroborating the necessity of including personalized RMDS in the procedure is highlighted. The Non-invasive Monitoring System and Health Assessment of the Elderly in a Smart Environment (RO-SmartAgeing) is an RMDS designed to address the improved preventative and proactive support for diminishing this risk and to provide suitable assistance for the elderly through a safe and efficient smart environment. Its comprehensive functionalities targeted supporting primary healthcare assistance, specific medical conditions—as the mental and emotional disorders post-SARS-CoV-2 infection—and enlarged access to aging-related information, together with customizable features, illustrated the match with the requirements included in the proposed procedure.

The digitalization of healthcare systems, particularly through Electronic Health Records (EHRs), presents both opportunities and challenges. This chapter delves into the transformative potential of integrating Artificial Intelligence (AI) with advanced cybersecurity measures in EHR systems. The impressive capabilities of AI models in data management, predictive analytics, and automation are explored for their role in enhancing patient outcomes and streamlining healthcare operations. The study addresses critical cybersecurity issues, including data breaches and ransomware, emphasizing the necessity of encryption, multi-factor authentication, and continuous monitoring. It examines how AI-driven threat detection and automated incident response can proactively safeguard sensitive patient data, also highlighting the challenges that may appear in the integration of AI into EHR systems along with addressing the need for robust interoperability standards and comprehensive data governance frameworks to mitigate cyber threats. The discussion extends toward the future vision that includes continuous innovation and strategic investment in AI and cybersecurity to create a more efficient, secure, and patient-centric healthcare environment. This comprehensive analysis highlights the synergistic potential of AI and cybersecurity in revolutionizing EHR systems and enhancing the overall quality of healthcare delivery.