Exploring biosensors: Distinctive features and emerging applications.

The shift toward decentralized cancer care has accelerated the demand for innovative technologies that enable continuous, remote monitoring of patients undergoing chemotherapy. Wearable biosensors have emerged as a promising solution, offering real-time tracking of physiological and biochemical markers critical to chemotherapy management. This review explores the integration of wearable biosensors into remote chemotherapy monitoring, examining their technological foundations, clinical applications, and impact on patient outcomes. It highlights how these devices enhance adherence, detect adverse effects early, and support timely clinical decisions in outpatient and home-based settings. Additionally, the paper addresses key implementation challenges, including technical limitations, patient usability, and regulatory considerations. Finally, it discusses future directions in sensor development, artificial intelligence integration, and policy adaptation to support scalable, personalized cancer care. Wearable biosensor technology holds transformative potential to redefine chemotherapy delivery within decentralized care models.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

The transition to renewable energy sources and the electrification of transportation have created an unprecedented demand for advanced energy storage technologies. The accelerating demand for efficient energy storage solutions has driven unprecedented innovation in materials science, with computational design emerging as a transformative approach for discovering next-generation battery and supercapacitor materials. This comprehensive review examines the latest developments in computational methodologies for energy storage material design, including density functional theory, machine learning approaches, and high-throughput screening techniques. Computational materials science has emerged as a powerful paradigm for accelerating energy storage innovation by enabling rapid screening of material properties and prediction of optimal compositions before experimental synthesis. We discuss the fundamental principles underlying computational materials discovery, their implementation across diverse energy storage systems, and the challenges associated with property prediction, structural optimisation, and experimental validation. Key applications spanning lithium-ion batteries, solid-state electrolytes, sodium-ion systems, and supercapacitor materials are analysed through recent breakthrough studies. The integration of artificial intelligence with quantum mechanical calculations, automated synthesis pathways, and multi-scale modelling represents promising frontiers for accelerating energy storage innovation. This review highlights both current achievements and future opportunities in developing predictive, interpretable, and efficient computational frameworks for energy storage applications.

In this modern era, wearable biosensors have emerged as a significant innovation in specialized personal healthcare. While smartphones and smartwatches today can easily measure vital signs and mobility, a new generation of wearable technology quickly emerges, allowing users to monitor their health metrics at the molecular level. Wearable electrochemical microneedle biosensors show the capability of detecting analytes and metabolites in interstitial fluid with minimal invasiveness. The use of microneedle sensing technology revolutionises biosensing techniques and opens new avenues for advancing current biosensors. In situ extraction, monitoring, and painless injection become possible through microneedle biosensors. However, there remains a need for improvement in detection accuracy and accessibility. This review begins with a discussion on the introduction and a comprehensive background of microneedle technology. It then explores different types of microneedles and fabrication methods. Various sensing modalities for microneedle biosensors, such as electrical, electrochemical, Raman, and colorimetric methods, are also discussed. Finally, the practical applications of wearable microneedle biosensors in various fields are examined, followed by a comprehensive conclusion and prospects.

Inclusive healthcare delivery has emerged as a strategic priority in response to rising global disability prevalence, accelerated population aging, and persistent disparities in access to quality medical services. Intelligent assistive technologies and smart healthcare devices are transforming traditional care models by enabling continuous monitoring, adaptive rehabilitation, and personalized clinical decision support across home-based, community, and institutional environments. Integration of artificial intelligence, Internet of Things (IoT) infrastructures, wearable biosensors, edge–cloud computing, and multimodal data fusion frameworks has created scalable ecosystems capable of supporting functional independence and proactive health management for vulnerable and underserved populations. This chapter presents a comprehensive examination of intelligent assistive systems, IoT-enabled wearable technologies, interoperability standards, Ambient Assisted Living environments, and personalized healthcare modeling through multimodal data streams. System-level architectures, human–machine interaction models, and edge-enabled analytics are analyzed to highlight pathways toward seamless integration within connected healthcare ecosystems. Critical ethical, regulatory, and socio-technical considerations including data governance, cybersecurity resilience, standardization frameworks, and equitable technology access are synthesized to address barriers to large-scale deployment. Emphasis was placed on scalable, inclusive, and patient-centered design principles that align technological innovation with sustainable healthcare equity objectives. By bridging device-level advancements with ecosystem-level integration strategies, this chapter contributes a unified conceptual framework for deploying intelligent, interoperable, and ethically governed smart healthcare solutions. The synthesis offers actionable insights for researchers, clinicians, policymakers, and technology developers seeking to advance inclusive, data-driven, and resilient healthcare systems suitable for diverse global populations.

In today’s world the rapid advancements in Micro-Electromechanical Systems (MEMS) and Nano technology have improved almost all the aspects of daily life routine with the help of different smart devices such as smart phones, compact electronic devices etc. The prime example of these emerging developments is the development of wireless sensors for healthcare procedures. One kind of these sensors is wearable bio-sensors. In this paper, the technologies of two types of bio-sensors (ECG, EMG) are investigated and also compared with traditional ECG, EMG equipment. We have taken SHIMMERTM wireless sensor platform as an example of wearable biosensors technology. We have investigated the systems developed for analysis techniques with SHIMMERTM ECG and EMG wearable bio-sensors and these biosensors are used in continuous remote monitoring. For example, applications in continuous health monitoring of elderly people, critical chronic patients and Fitness & Fatigue observations. Nevertheless, early fall detection in older adults and weak patients, treatment efficacy assessment. This study not only provides the basic concepts of wearable wireless bio-sensors networks (WBSN), but also provides basic knowledge of different sensor platforms available for patient’s remote monitoring. Also various healthcare applications by using bio-sensors are discussed and in last comparison with traditional ECG and EMG is presented.

Wearable film-based smart biosensors have been developed for real-time biomolecules detection. Particularly, interfacial co-assembly of reduced graphene oxide-prussian blue (PB-RGO) film through electrostatic interaction has been systematically studied by controllable pH values, achieving optimal PB-RGO nanofilms at oil/water (O/W) phase interface driven by minimization of interfacial free energy for wearable biosensors. As a result, as-prepared wearable biosensors of PB-RGO film could be easily woven into fabrics, exhibiting excellent glucose sensing performance in amperometric detection with a sensitivity of 27.78 µA mM-1cm-2 and a detection limit of 7.94μM, as well as impressive mechanical robustness of continuously undergoing thousands of bending or twist. Moreover, integrated wearable smartsensing system could realize remotely real-time detection of biomarkers in actual samples of beverages or human sweat via cellphones. Prospectively, interfacial co-assembly engineering driven by pH-induced electrostatic interaction would provide a simple and efficient approach for acquiring functional graphene composites films, and further fabricate wearable smartsensing devices in health monitoring fields.

Polymeric biomaterials, particularly electrospun nanofibers, are increasingly central to the development of advanced wound dressings capable of supporting tissue regeneration while enabling real-time physiological monitoring. Chronic wounds associated with diabetes, vascular diseases, and cancer require continuous and personalized management, prompting the convergence of electrospun polymeric scaffolds with wearable biosensors and artificial intelligence (AI). These next-generation smart wound dressings utilize biocompatible polymer matrices functionalized with responsive sensing elements to monitor pH, temperature, moisture, oxygen saturation, and inflammatory biomarkers in situ. Molecular-level interactions between polymeric components and biological tissues facilitate both therapeutic delivery and diagnostic functionality. AI, including deep and federated learning, enhances these systems by enabling data-driven prediction of healing trajectories and personalized interventions. Key advances in flexible electronics, self-powered systems, and closed-loop feedback mechanisms further enhance clinical applicability. However, challenges remain, including the biochemical stability of sensors in enzyme-rich environments, secure wireless communication, and the lack of standardized datasets and clinical validation frameworks. This review critically examines recent progress in AI-integrated polymeric wound care systems, emphasizing the design of functional polymeric scaffolds, biosensor-polymer interfaces, and future directions, including biosensor miniaturization, multi-omics data integration, and scalable cloud-based platforms. A collaborative roadmap is proposed to advance these intelligent biomaterial systems toward clinical translation in chronic wound care.

Development strategies of conducting polymer-based electrochemical biosensors for virus biomarkers: Potential for rapid COVID-19 detection

Artificial Intelligence in Cardiology: Applications and Obstacles

Objective : To analyze foreign experience in the use of artificial intelligence (AI) in oncology, as well as examine current advances in AI and its impact on clinical practice. Material and methods. A systematic review of foreign literature was carried out, current AI programs and technologies in oncology were analyzed. The total number of identified records via the PubMed/MEDLINE search was 7680. After publication selection in accordance with the PRISMA guidelines, 32 studies that met all criteria were randomly included in the review and formed the basis for the analysis. Results . AI demonstrates high efficiency in cancer diagnostics, including early detection of tumors, analysis of medical images and pathological data. A literature review on the use of AI models in oncology revealed exponential growth from 2010 to 2022, confirming the active development of the field. Diagnostic and treatment reports generated by the AI technologies indicated a comparable level of accuracy to that of experienced oncologists; they also revealed the ability to improve clinical outcomes. Furthermore, the introduction of AI stimulates the development of personalized treatment, increased patient adherence to therapy and optimization of the work of medical organizations. AI’s impact was revealed on physicians (reduced diagnostic and treatment errors), patients (personalized support), and hospitals (smart management systems). Conclusion . AI is becoming an integral part of modern oncology, offering new opportunities to improve diagnostics, treatment, outcome prediction, and patient support. A study of the dynamics of publications and AI models indicates that the use of AI in clinical oncology is rapidly developing, opening up new prospects for the creation of smart hospitals, simplification of medical data exchange, development of personalized medicine, and improvement of the quality of patient care. The integration of AI with emerging technologies such as wearable devices and multimodal analysis promises to revolutionize oncology practice.

Artificial intelligence (AI) is rapidly emerging as a transformative force in oncology, offering significant benefits across the cancer care continuum. Through advanced image analysis, AI enables earlier and more accurate diagnosis by detecting subtle abnormalities in radiology, pathology, and liquid biopsy data that may elude conventional assessment. Integrating multi-omics, clinical, and imaging datasets, AI supports highly personalized treatment planning, predicting therapeutic responses and guiding the selection of targeted agents and immunotherapies. Machine learning models also facilitate rapid drug discovery and repurposing, and improve patient access to clinical trials by matching tumor molecular profiles with trial eligibility criteria. In local therapies, AI enhances surgical navigation and radiotherapy planning, increasing precision while sparing healthy tissues. Continuous patient monitoring through wearable devices, electronic health records, and laboratory data allows AI systems to identify complications or recurrence earlier than standard follow-up methods. In supportive and palliative care, AI-driven tools anticipate side effects, optimize symptom management, and provide language, literacy, and psychological support. Furthermore, AI-enabled tele-oncology and translation services expand cancer care to underserved populations, addressing disparities in access. While ethical, regulatory, and technical challenges remain, the integration of AI into oncology holds immense promise for improving diagnostic accuracy, therapeutic efficacy, and quality of life for cancer patients worldwide.

The integration of Artificial Intelligence (AI) in healthcare is revolutionizing diagnostics, offering important opportunities while emerging challenges. This essay explores the potential and obstacles of AI-based diagnostics. Key problems include technological and ethical issues such as data privacy, compatibility of devices , and regulatory issues. Ethical considerations also arise around patient privacy, accountability for AI errors, legal frameworks, and algorithm transparency. Despite these challenges, AI showcases substantial opportunities for the improvement of diagnostics and treatment techniques. Its capabilities include earlier disease detection, personalized treatment pathways, and healthcare professional support, particularly in radiology, pathology, and genomics. Additionally, AI can help and reduce the cost of healthcare access by streamlining resources, optimizing staff deployment, and facilitating remote monitoring and telemedicine, especially for developing countries. With the analysis of data from wearable devices and enabling remote consultations, AI helps us in the proactive management of chronic conditions. AI-based diagnostics can help overcome long term healthcare challenges, enhancing precision and treatment efficiency. A balanced approach is essential, addressing ethical, privacy, and legal considerations while emphasizing AI’s role as a support for healthcare professionals. Future research should focus on AI algorithm transparency, ethics, and the expansion of telemedicine, especially in underserved areas.

The role of AI in transforming psychiatric-mental health care: Enhancing the role of psychiatric-mental health nurse practitioners.

The integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. Artificial intelligence offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing, and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex datasets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.