Healthcare Applications Research Articles

Blockchain barriers in hospitals: a stakeholder theoretic perspective

PurposeGrounded in stakeholder theory, this study aims to examine the barriers to blockchain adoption in hospitals in developing countries. It also aimed to explore the interrelationships among these barriers and investigate how the perceptions of clinical and non-clinical professionals in the healthcare industry differ regarding these barriers.Design/methodology/approachThe study context was hospitals in India and proceeded in three phases. First, barriers affecting blockchain application in healthcare were shortlisted using a systematic literature review. In the second phase, a multi-round Delphi study with clinical and non-clinical healthcare experts was conducted to screen and validate the barriers identified in the first phase. Finally, the barriers were ranked and categorized into causal and effect groups using the Grey-DEMATEL technique in the last phase of the study.FindingsThe findings reveal variance in the viewpoints of clinical and non-clinical professionals regarding influential barriers. Overall, the most significant causal barriers were a shortage of IT skills and a lack of standards for patient data management standards in Indian hospitals. Additionally, the study identified the lack of a well-defined strategy for blockchain infrastructure deployment and limited support from hospital management as effect barriers.Originality/valueTo the best of the authors’ knowledge, this is the first attempt to use the integrated Delphi-DEMATEL approach to explore blockchain adoption barriers in hospitals from the expert’s point of view.

Evaluating the Effect of Climate on Viral Respiratory Diseases Among Children Using AI

Background: Respiratory viral infections (RVIs) exhibit seasonal patterns influenced by biological, ecological, and climatic factors. Weather variables such as temperature, humidity, and wind impact the transmission of droplet-borne viruses, potentially affecting disease severity. However, the role of climate in predicting complications in pediatric RVIs remains unclear, particularly in the context of climate-change-driven extreme weather events. Methods: This retrospective cohort study analyzed 1610 hospitalization records of children (0–18 years) with lower respiratory tract infections in Rome, Italy, between 2018 and 2023. Viral pathogens were identified using nasopharyngeal molecular testing, and weather data from the week preceding hospitalization were collected. Several machine learning models were tested, including logistic regression and random forest, comparing the baseline (demographic and clinical) models with those including climate variables. Results: Logistic regression showed a slight improvement in predicting severe RVIs with the inclusion of weather variables, with accuracy increasing from 0.785 to 0.793. Average temperature, dew point, and humidity emerged as significant contributors. Other algorithms did not demonstrate similar improvements. Conclusions: Climate variables can enhance logistic regression models’ ability to predict RVI severity, but their inconsistent impact across algorithms highlights challenges in integrating environmental data into clinical predictions. Further research is needed to refine these models for use in reliable healthcare applications.

Beyond Human Touch: Integrating Soft Robotics with Environmental Interaction for Advanced Applications

Soft robotics is an emerging field dedicated to the design and development of robots with soft structures. Soft robots offer unique capabilities in terms of flexibility, adaptability, and safety of physical interaction, and therefore provide advanced collaboration between humans and robots. The further incorporation of soft actuators, advanced sensing technologies, user-friendly control interfaces, and safety considerations enhance the interaction experience. Applications in healthcare, specifically in rehabilitation and assistive devices, as well as manufacturing, show how soft robotics has revolutionized human–robot collaboration and improved quality of life. Soft robotics can create new opportunities to enhance human well-being and increase efficiency in human–robot interactions. Nevertheless, challenges persist, and future work must focus on overcoming technological barriers while increasing reliability, refining control methodologies, and enhancing user experience and acceptance. This paper reviews soft robotics and outlines its advantages in scenarios involving human–robot interaction.

Ranking of Machine Learning Algorithms Used in Disease Prediction: A Review-based Approach

There are remarkable improvements in the healthcare sector particularly in patient care, maintaining and protecting the data, and saving administrative and operating costs, etc. Among the various functions in the healthcare sector, disease diagnosis is considered as the foremost function because it saves a life at the correct time. Early detection of diseases helps in disease prevention, letting the patients get vigorous and effective treatment and saving their lives. Several techniques were suggested by the researchers for disease prediction. Many literatures have been witnessed on disease prediction. This article reviews several articles systematically and compares various machine learning (ML) algorithms for disease prediction, including the Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR) algorithms. A thorough analysis is presented based on the number of publications year-wise, disease-wise, and also based on the performance metrics. This review thoroughly analyzes and compares various ML techniques applied in disease prediction, focusing on classification algorithms commonly employed in healthcare applications. From the systematic review, a multi objective optimization method named Grey Relational Analysis (GRA) is used to rank the ML algorithms using their performance metrics. The results of this paper help the researchers to have an insight into the disease prediction domain. Also, the performance of various ML algorithms aids the researchers to choose a better methodology to predict a disease.

Essential Oils as Alternative Green Broad-Spectrum Biocides

Natural compounds from plants represent suitable options to replace synthetic biocides when employed against microorganisms in various applications. Essential oils (EOs) have attracted increased interest due to their biocompatible and rather innocuous nature, and complex biological activity (fungicide, biocide and anti-inflammatory, antioxidant, immunomodulatory action, etc.). EOs are complex mixtures of derived metabolites with high volatility obtained from various vegetal parts and employed to a great extent in different healthcare (natural cures, nutrition, phyto- and aromatherapy, spices) and cosmetics applications (perfumery, personal and beauty care), as well as in cleaning products, agriculture and pest control, food conservation and active packaging, or even for restauration and preservation of cultural artifacts. EOs can act in synergy with other compounds, organic and synthetic as well, when employed in different complex formulations. This review will illustrate the employment of EOs in different applications based on some of the most recent reports in a systematic and comprehensive, though not exhaustive, manner. Some critical assessments will also be included, as well as some perspectives in this regard.

Encryption and Security Enhanced Middleware SOA Solutions for High-Security Applications

This paper examines the security enhancements for middleware layer that play a crucial role in protecting information. In the digital age, the integration of security measures with software architecture is paramount, especially for industries such as banking, healthcare, and high-security applications. These sectors handle sensitive data that requires robust protection from cyber threats. The Service-Oriented Architecture (SOA) is a widely adopted architectural pattern that facilitates the design of modular, interoperable systems. However, to address the escalating risks associated with cybercrime and data breaches, traditional SOA solutions must evolve by incorporating encryption and other advanced security measures. This paper explores how encryption and security- enhanced middleware enhances the overall security for these high-stakes industries. Keywords: SOA, OIC, Webservices, XML, TLS, SSL, Encryption, Data encryption, Encryption at rest. SAML, SSO, Application Security

Artificial Intelligence in Nursing Practice: Legal Aspects and Transformation of the Profe ssional Role of Nurses

The article examines the current issue of implementing artificial intelligence (AI) technologies in modern medical practice with a special focus on the role of nursing staff. The key areas of AI application in healthcare, the existing regulatory framework in the Russian Federation, and ethical aspects of using intelligent systems in medicine are analyzed. Particular attention is paid to the transformation of nurses’ professional competencies in the context of healthcare digitalization. Based on the analysis of current practices and technology development prospects, recommendations are proposed for adapting educational programs and professional standards for nursing staff.

Exploring Robotic Nursing: A Comprehensive Systematic Review of Socially Assistive Robots within the Healthcare Professions

ABSTRACT Purpose The objective is to evaluate empirical literature on Socially Assistive Robots (SARs) in nursing, identifying strengths, weaknesses, and avenues for future research and practice. This article explores studies investigating robotic applications in healthcare, specifically nursing. Design The systematic review using PRISMA guidelines includes 19 relevant studies from Medline, PubMed, and Cochrane, presenting insights into the potential of SARs in addressing patients’ mental health and well-being. Methods The eligibility criteria for this review included publications from 2015 to 2024, full-text articles employing either qualitative or quantitative methodologies, articles written in English, and those published in peer-reviewed academic journals. Excluded were single-case studies, unpublished theses or dissertations, and studies not published in peer-reviewed journals. The search employed the terms “robotic nursing” and “future,” utilizing Medical Subject Headings (MeSH) terminology for precise retrieval. Findings The current literature has limitations in generalizability, breadth, and quantification of health-related outcomes. Areas of exploration, including diverse robot types, therapies, and geographical contexts, offer promising directions for future research. Conclusions The article concludes that although SARs show promise in healthcare, particularly in nursing, more comprehensive research is needed to establish their efficacy across a wider range of applications and settings. Clinical Evidence Current studies indicate that SARs may play a valuable role in supporting patients’ mental health and well-being, though further investigation is required to determine their broader impact and effectiveness.

Transforming Healthcare in Low-Resource Settings With Artificial Intelligence: Recent Developments and Outcomes.

Artificial intelligence now encompasses technologies like machine learning, natural language processing, and robotics, allowing machines to undertake complex tasks traditionally done by humans. AI's application in healthcare has led to advancements in diagnostic tools, predictive analytics, and surgical precision. This comprehensive review aims to explore the transformative impact of AI across diverse healthcare domains, highlighting its applications, advancements, challenges, and contributions to enhancing patient care. A comprehensive literature search was conducted across multiple databases, covering publications from 2014 to 2024. Keywords related to AI applications in healthcare were used to gather data, focusing on studies exploring AI's role in medical specialties. AI has demonstrated substantial benefits across various fields of medicine. In cardiology, it aids in automated image interpretation, risk prediction, and the management of cardiovascular diseases. In oncology, AI enhances cancer detection, treatment planning, and personalized drug selection. Radiology benefits from improved image analysis and diagnostic accuracy, while critical care sees advancements in patient triage and resource optimization. AI's integration into pediatrics, surgery, public health, neurology, pathology, and mental health has similarly shown significant improvements in diagnostic precision, personalized treatment, and overall patient care. The implementation of AI in low-resource settings has been particularly impactful, enhancing access to advanced diagnostic tools and treatments. AI is rapidly changing the healthcare industry by greatly increasing the accuracy of diagnoses, streamlining treatment plans, and improving patient outcomes across a variety of medical specializations. This review underscores AI's transformative potential, from early disease detection to personalized treatment plans, and its ability to augment healthcare delivery, particularly in resource-limited settings.

IoT Applications in Healthcare and Industry: Current State, Challenges, and Future Perspectives

The Internet of Things (IoT) has revolutionized various domains by enabling seamless communication between devices and systems. The Internet of Things (IoT) has revolutionized various domains by enabling seamless communication between devices and systems. This technological marvel connects the physical and digital worlds, creating smarter environments and driving unprecedented innovation. From healthcare to industry, transportation to agriculture, IoT is reshaping how we interact with technology, making processes faster, more efficient, and remarkably intuitive. In healthcare, IoT empowers patients with remote monitoring, smart devices, and real-time data insights, ensuring better outcomes and accessible care. In industries, IoT drives automation, predictive maintenance, and optimized supply chains, reducing costs and boosting productivity. This paper explores the applications of IoT in healthcare and industry, highlighting its current state, challenges, and future prospects. A comprehensive literature review is conducted to understand the advancements, and potential solutions to key challenges such as security, interoperability, and data management are discussed. Finally, the paper presents a roadmap for future research to maximize IoT's potential in these critical sectors

Smart Healthcare Prediction Using Machine Learning

In this paper, the utilization of machine learning techniques in the healthcare system is introduced. As the healthcare industry generates increasingly vast amounts of data daily, manual processing by humans becomes impractical for prompt disease diagnosis and treatment decisions. To address this challenge, data management techniques and machine learning algorithms are explored in healthcare applications to facilitate more accurate decision-making processes. Detailed descriptions of medical data are provided, enhancing various facets of healthcare applications through the adoption of this cutting-edge technology. Naïve Bayes machine learning algorithm is employed to train the machine for predicting various diseases, extracting new patterns from extensive datasets to enable predictive analysis and derive knowledge linked to these patterns. A key focus lies in acquiring data automatically or semi-automatically, highlighting the significance of this process. Key Words: Machine Learning, Healthcare, Naïve Bayes Algorithm, Predictive Models, Data prediction.

Perspectives of Patients Regarding Artificial Intelligence and Its Application in Healthcare: A Qualitative Study.

Artificial intelligence integration into healthcare has gained significant attention in recent years, with its use ranging from disease diagnosis to surgical assistance. While artificial intelligence's potential to improve patient outcomes and optimise patient care is undeniable, concerns regarding privacy, transparency, and the potential for medical errors persist. To take full advantage of artificial intelligence's transformative abilities, understanding patient perceptions and attitudes towards its integration into medicine is crucial for ethical considerations and health outcomes. This study aimed to describe patients' perceptions of medical artificial intelligence and its integration into the healthcare system, drawing attention to a crucial yet understudied aspect of artificial intelligence adoption in Kazakhstan. Descriptive qualitative design. From February to March 2024, the researchers conducted semi-structured interviews amongst 13 patients. The interviews were audio-recorded, transcribed, translated, and then analysed using a thematic analysis approach. The study adhered to the COREQ guidelines. Five themes emerged from 13 interviews: the benefits of artificial intelligence on patient care, the importance of human factors on patient care over artificial intelligence, revolutionising patient care delivery through artificial intelligence, patient education and artificial intelligence, and balancing technology and human interaction in artificial intelligence-driven intervention. Patient perceptions of artificial intelligence integration into healthcare were primarily positive. Nevertheless, patients prefer artificial intelligence as a supplementary tool under human supervision due to risks such as possible medical errors and violations of patient privacy. Patients provided the data for this study. The researchers interviewed them about their perceptions of medical artificial intelligence and its integration into the healthcare system. The patients or the public contributed nothing to the other aspects of the study.

Implementation and evaluation of digital twin framework for Internet of Things based healthcare systems

AbstractThe integration of digital twins (DTs) in healthcare is critical but remains limited in real‐time patient monitoring due to challenges in achieving low‐latency telemetry transmission and efficient resource management. This paper addresses these limitations by presenting a novel cloud‐based DT framework that optimises real‐time healthcare monitoring, providing a timely solution for critical healthcare needs. The framework incorporates a Pyomo‐based dynamic optimisation model, which reduces telemetry latency by 32% and improves response time by 52%, surpassing existing systems. Leveraging low‐cost, low‐latency multimodal sensors, the system continuously monitors critical physiological parameters, including SpO2, heart rate, and body temperature, enabling proactive health interventions. A DT definition language (Digital Twin Definition Language)‐based time series analysis and twin graph platform further enhance sensor connectivity and scalability. Additionally, the integration of machine learning (ML) strengthens predictive accuracy, achieving 98% real‐time accuracy and 99.58% under cross‐validation (cv = 20) using the XGBoost algorithm. Empirical results demonstrate substantial improvements in processing time, system stability, and learning capacity, with real‐time predictions completed in 17 ms. This framework represents a significant advancement in healthcare monitoring, offering a responsive and scalable solution to latency and resource constraints in real‐time applications. Future research could explore incorporating additional sensors and advanced ML models to further expand its impact in healthcare applications.

Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging

This article presents a thorough examination of the progress and limitations in the application of Natural Language Processing (NLP) and Machine Learning (ML), particularly Deep Learning (DL), in the healthcare industry. This paper examines the progress and limitations in the utilisation of Natural Language Processing (NLP) and Machine Learning (ML) in the healthcare field, specifically in relation to Electronic Medical Records (EMRs). The review also examines the incorporation of Natural Language Processing (NLP) and Machine Learning (ML) in medical imaging as a supplementary field, emphasising the transformative impact of these technologies on the analysis of healthcare data and patient care. This review attempts to analyse both fields in order to offer insights into the current state of research and suggest potential chances for future advancements. The focus is on the use of these technologies in Electronic Medical Records (EMRs) and medical imaging. The review methodically detects, chooses, and assesses literature published between 2015 and 2023, utilizing keywords pertaining to natural language processing (NLP) and healthcare in databases such as SCOPUS. After applying precise inclusion criteria, 100 papers were thoroughly examined. The paper emphasizes notable progress in utilizing NLP and ML methodologies to improve healthcare decision-making, extract information from unorganized data, and evaluate medical pictures. The key findings highlight the successful combination of natural language processing (NLP) and image processing to enhance the accuracy of diagnoses and improve patient care. The study also demonstrates the effectiveness of deep learning-based NLP pipelines in extracting valuable information from electronic medical records (EMRs). Additionally, the research suggests that NLP has the potential to optimize the allocation of medical imaging resources. The identified gaps encompass the necessity for scalable and practical implementations, improved interdisciplinary collaboration, the consideration of ethical factors, the analysis of longitudinal patient data, and the customization of approaches for specific medical situations. Subsequent investigations should focus on these deficiencies in order to fully exploit the capabilities of natural language processing (NLP) and machine learning (ML) in the healthcare sector, consequently enhancing patient outcomes and the delivery of healthcare services.

Digital twin for EEG seizure prediction using time reassigned multisynchrosqueezing transform-based CNN-BiLSTM-attention mechanism model

Abstract The prediction of epileptic seizures is a classical research problem, representing one of the most challenging tasks in the analysis of brain disorders. There is active research into digital twins (DT) for various healthcare applications, as they can transform research into customized and personalized healthcare. The widespread adoption of DT technology relies on ample patient data to ensure precise monitoring and decision-making, leveraging Machine Learning (ML) and Deep Learning (DL) algorithms. Given the non-stationarity of EEG recordings, characterized by substantial frequency variations over time, there is a notable preference for advanced time-frequency methods in seizure prediction. This research proposes a DT-based seizure prediction system by applying an advanced time-frequency analysis approach known as Time-Reassigned MultiSynchroSqueezing Transform (TMSST) to EEG data to extract patient-specific impulse features and subsequently, a Deep Learning strategy, CNN-BiLSTM-Attention mechanism model is utilized in learning and classifying features for seizure prediction. The proposed architecture is named as “Digital Twin-Net”. By estimating the group delay in the time direction, TMSST produces the frequency components that are responsible for the EEG signal’s temporal behavior and those time-frequency signatures are learned by the developed CNN-BiLSTM-Attention mechanism model. Thus the combination acts as a digital twin of a patient for the prediction of epileptic seizures. The experimental results showed that the suggested approach achieved an accuracy of 99.70% when tested on 23 patients from the publicly accessible CHB-MIT dataset. The proposed method surpasses previous solutions in terms of overall performance. Consequently, the suggested method can be regarded as an efficient approach to EEG seizure prediction.

A Tertiary Study for Process Mining

Background: This tertiary study lists the secondary studies published in the process mining domain and provides an analysis related to a set of research questions. It is the first tertiary study in this area. The objective is to provide information about the available secondary studies in process mining, respond to research questions relating to the thematic areas covered in the studies, as well as trends regarding their quality, and report on findings for publication venues, citations, guidelines used, and demographics. Method: A tertiary study based on systematic secondary studies published up to March 2023. A total of 25 secondary studies related to process mining have been identified following the application of inclusion/exclusion criteria and quality assessment. Results: The most popular thematic areas addressed are technologies and applications for process mining and healthcare. The secondary studies in process mining have a medium quality score of 3.5. The guidelines introduced by Kitchenham over the years are preferred in secondary studies in this field. There is no trend related to the number of primary studies included in secondary studies in process mining. Conclusion: Although numerous secondary studies exist for process mining, there is still room for more research, specifically in the areas highlighted in this study. Future researchers can use this study for reference, and they can also use the listed research topics to dive deep into the issues identified.

Integrating gait and speech dynamics methodologies for enhanced stuttering detection across diverse datasets

<span lang="EN-US">Stuttering manifests as involuntary interruptions in the fluency of speech, often involving repetitions, prolongations, or blocks of sounds or syllables. These disruptions can significantly impact effective communication and psychosocial well-being. This research introduces a comprehensive system for speech impairment detection and gait analysis. Speech impairment, with a primary focus on stammer recognition, presents a multifaceted challenge in the field of speech processing. Stammers can manifest in various forms and detecting them accurately is a complex task. Our proposed methodology revolves around the development of StEnsembleNet, a neural network designed to learn spectral features at the frame level, enabling precise and efficient identification of speech impediments. Additionally, we extend our system's capabilities to the domain of gait analysis, leveraging a novel adaptive graph topology convolution network (AGT-ConvNet) for skeletal motion and visually enhanced topological learning to adapt to diverse visual environments and enhance the recognition of gait patterns. This research not only contributes to the field of speech therapy but also offers potential applications in healthcare and motion analysis.</span>

Personalized Chatbot Responses using Reinforcement Learning and User Modeling

The research focuses on chatbot interaction enrichment using reinforcement learning and user modeling. It aims to develop a personalized RL-based response generation framework for the optimization of satisfaction, engagement, and completion rates for the users. Anonymized historical interaction data from 500 users were collected to generalize user-profiles and contextual models. This was done by extracting features and observing and evaluating the control and experimental groups to test the efficacy of the working personalized system. Results indicated an overall increase of 35 percent in user satisfaction, 50 percent in session duration, and 25 percent in rates of completion of tasks against the traditional rule-based system. The results are very much in line with current literature on the improvements personalization can bring to the user's experience across many domains. The results from this study thus propose that personal AI systems powered with fine-grained models of users and reinforcement learning could obtain more engaging and efficient user interactions. The result has further-reaching implications for, for example, e-learning, customer service, and healthcare applications.

Metabolite Profile and Antioxidant Activities of Trikatu, Black Pepper, Javanese Long Pepper, and Red Ginger Essential Oils

BackgroundTrikatu (TR) is an Ayurvedic formulation that consists of three pungent medicinal ingredients: long pepper fruit, black pepper, and ginger rhizome. However, the strong pungency of the TR constituents poses challenges for oral administration. Therefore, the formulation of TR in the form of essential oils for aromatherapy could be considered to overcome this challenge. Aim of the studyThis study aimed to determine the contribution of black pepper (BP), javanese long pepper (JLP), and red ginger (RG) to the GC-MS metabolite profile of trikatu (TR) essential oil and to investigate their in vitro antioxidant activities. MethodsThe essential oils from BP, JLP, and RG were extracted using the stahl hydro-distillation method. Phytochemical profiling was carried out by gas chromatography-mass spectrometry (GC-MS). Principal Component Analysis (PCA) was performed with the help of R packages of factoMiner and factoExtra to examine the metabolite distributions. Antioxidant activities of TR, BP, JLP, and RG essential oils was determined using the DPPH free radical scavenging assay. ResultsThe GC-MS analysis of BP, JLP, RG, and TR essential oils revealed the presence of 11 common compound, including linalool, caryophyllene, and beta-bisabolene. PCA showed different patterns of compound abundance across the samples. For example, linalool was more abundant in TR samples, while gamma-bisabolene was more in JLP samples. The antioxidant activities of TR, BP, JLP, and RG essential oils were significantly different, with RG showing the lowest IC50 value 2.73±0.09 mg/mL, indicating higher antioxidant potential compared to BP and JLP. ConclusionsThis study highlights the unique metabolite profiles in RG, BP, JLP, and TR essential oils, which contribute significantly to their bioactive properties, particularly antioxidant activity. RG essential oil plays a significant role in enhancing the antioxidant potential of TR. These insights support broader applications in aromatherapy, wellness, and healthcare, where TR essential oil can be used for antioxidant support, stress relief, and as a natural supplement. Future research may further optimize these essential oils for targeted therapeutic and preventive applications.

Exploring ChatGPT in clinical inquiry: a scoping review of characteristics, applications, challenges, and evaluation.

Recent advancements in generative AI, exemplified by ChatGPT, hold promise for healthcare applications such as decision-making support, education, and patient engagement. However, rigorous evaluation is crucial to ensure reliability and safety in clinical contexts. This scoping review explores ChatGPT's role in clinical inquiry, focusing on its characteristics, applications, challenges, and evaluation. This review, conducted in 2023, followed PRISMA-ScR guidelines (Supplemental Digital Content 1, http://links.lww.com/MS9/A636). Searches were performed across PubMed, Scopus, IEEE, Web of Science, Cochrane, and Google Scholar using relevant keywords. The review explored ChatGPT's effectiveness in various medical domains, evaluation methods, target users, and comparisons with other AI models. Data synthesis and analysis incorporated both quantitative and qualitative approaches. Analysis of 41 academic studies highlights ChatGPT's potential in medical education, patient care, and decision support, though performance varies by medical specialty and linguistic context. GPT-3.5, frequently referenced in 26 studies, demonstrated adaptability across diverse scenarios. Challenges include limited access to official answer keys and inconsistent performance, underscoring the need for ongoing refinement. Evaluation methods, including expert comparisons and statistical analyses, provided significant insights into ChatGPT's efficacy. The identification of target users, such as medical educators and nonexpert clinicians, illustrates its broad applicability. ChatGPT shows significant potential in enhancing clinical practice and medical education. Nevertheless, continuous refinement is essential for its successful integration into healthcare, aiming to improve patient care outcomes, and address the evolving needs of the medical community.