Healthcare Technology Letters最新文献

{"title":"Trustworthy AI in Telehealth: Navigating Challenges, Ethical Considerations, and Future Opportunities for Equitable Healthcare Delivery","authors":"Seyyed Ali Zendehbad,&nbsp;Jamal Ghasemi,&nbsp;Farid Samsami Khodadad","doi":"10.1049/htl2.70020","DOIUrl":"10.1049/htl2.70020","url":null,"abstract":"<p>Trustworthy artificial intelligence (TAI) will transform telehealth by providing safe, transparent, and ethically compliant systems that enhance clinician decision-making and patient relationships. This systematic review examines how TAI and large language models (LLMs), including large language model meta ai (LLaMA), can be integrated into telehealth systems, their role in optimizing e-consultation workflows, and their capacity to support personalized care through data collected by wearable biosensors and biological microelectromechanical systems (BioMEMS). These devices monitor physiological and behavioral data, such as heart rate, blood pressure, and emotional state. TAI enables effective diagnostics and targeted treatment by combining various information sources, including biosensor readings, patient history, and cognitive data. Firmware integrity plays a crucial role in ensuring security, reliability, and continuous data encryption. This review analyses 135 papers (October 2020-March 2025) from databases like IEEE Xplore, PubMed, and Scopus to demonstrate TAI's potential to enhance resource use and patient engagement. However, widespread adoption depends on overcoming technical challenges, improving firmware reliability, strengthening data security, and addressing ethical concerns. This review offers valuable guidance for engineers, system architects, and healthcare providers to create a sensitive and effective telehealth ecosystem.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12485827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Model of the First Trimester Evaluation of Foetal Movements and Their Outcomes via Explainable Artificial Intelligence: A Multicentric Study","authors":"Manohar Pavanya,&nbsp;Krishnaraj Chadaga,&nbsp;Vennila J,&nbsp;Akhila Vasudeva,&nbsp;Bhamini Krishna Rao,&nbsp;Shashikala K. Bhat","doi":"10.1049/htl2.70014","DOIUrl":"10.1049/htl2.70014","url":null,"abstract":"<p>Foetal outcomes with reduced foetal movements in the later pregnancy are widely reported. We intend to quantify early foetal movements (FMs) through a checklist and their foetal outcomes via explainable artificial intelligence. It is a prospective observational study of 356 foetuses in the first trimester, and we were able to screen only 230 foetuses for early foetal growth restriction (FGR). Of which 26 were FGR and 204 were normal and were identified from the dataset using non-probability convenience sampling techniques. JASP 0.18.3, Jamovi 2.3.21, and Google Collaboratory were used to construct the predictive model. Ultrasound scores of more than 8 had favourable indicators of a normal foetus. CatBoost had the highest accuracy and recall of 87; the highest precision of 79 was given by random forest (RF), decision tree (DT), K-nearest neighbour (KNN), and CatBoost; and the F1 score of 83 was given by CatBoost. The lowest Hamming loss of 0.13 was obtained via CatBoost. The highest Jaccard score of 0.87 was by CatBoost. The stacked model has an accuracy of 89, a precision of 79, and a recall of 83. Shapley additive explanations (SHAP), local interpretable model-agnostic explanations (LIME), QLattice, and Anchor also provided good explanations. The created model can serve as a warning tool to obstetricians to make timely medical decisions.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145081924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI-Enabled, Patient-Centred Digital Platform for Integrated Chronic Heart Failure Management: Architecture, Validation and Clinical Insights","authors":"George Petridis,&nbsp;Apostolia Karabatea,&nbsp;Constantinos Bakogiannis,&nbsp;Emmanouil S. Rigas,&nbsp;Haridimos Kondylakis,&nbsp;Alexander Berler,&nbsp;Konstantinos Kangelidis,&nbsp;Nikolaos Malamas,&nbsp;Angelina Kouroubali,&nbsp;Dimitrios Katehakis,&nbsp;Vassilios Vassilikos,&nbsp;Panagiotis D. Bamidis,&nbsp;Antonios Billis","doi":"10.1049/htl2.70015","DOIUrl":"10.1049/htl2.70015","url":null,"abstract":"<p>Healthcare systems across Europe and globally are increasingly challenged by the need to deliver high-quality, coordinated care for complex patient populations, such as those living with chronic heart failure (CHF). Many national healthcare policies consider the adoption and implementation of patient-centred and interoperable information communication technologies-enabled solutions offered in a single digital platform as a key facilitator towards the transition to integrated and coordinated care. Aiming to support CHF patients and to assist their management, in this paper, we present CareCardia, a modular digital solution designed to support the comprehensive management of CHF. CareCardia offers an interoperable ecosystem that connects healthcare professionals, informal caregivers and patients along a unified CHF care pathway spanning across diagnosis, acute care and jointly managed long-term care. Specifically, CareCardia integrates state-of-the-art, clinical evidence-based technologies such as a clinical decision support system and an exergaming platform that will follow patients through the CHF journey. This paper outlines the system architecture and core functionalities of CareCardia prototype. We also present early findings from the initial exploration of the tool, discussing its anticipated impact on CHF and its potential to foster patient empowerment across the continuum of care.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Challenges in the Healthcare Industry: A Systematic Review of Recent Evidence","authors":"Esmaeil Mehraeen,&nbsp;Haleh Siami,&nbsp;Sarah Montazeryan,&nbsp;Reza Molavi,&nbsp;Akram Feyzabadi,&nbsp;Iman Parvizy,&nbsp;Zeynab Ataei Masjedlu,&nbsp;Maryam Naseri Dehkalani,&nbsp;Sanam Mahmoudi,&nbsp;Alihasan Ahmadipour","doi":"10.1049/htl2.70017","DOIUrl":"10.1049/htl2.70017","url":null,"abstract":"<p>While AI is essential to the development of electronic health, it has challenges that, if resolved, might improve the standard of healthcare services. The purpose of this study is to classify and identify these issues in the healthcare field. The study utilised a systematic review approach, drawing data from the Scopus, Web of Science, and PubMed databases. The search results were imported into EndNote software, and experienced experts reviewed the relevant articles. The selection criteria focused on original research articles in English, published between 2019 and July 2024, that provided full text and sufficient data on AI challenges. Forty-seven articles were included in the final analysis out of the 1453 that were identified. There were 17 categories for the obstacles, and the most common ones were technical challenges (29.8%), technological adoption (25.5%) and reliability and validity (23.4%). There are 24 categories into which the healthcare domains were divided. This article emphasises the critical importance of addressing technical challenges, enhancing reliability and validity, safeguarding patient data, and overcoming the lack of knowledge and understanding of artificial intelligence among patients and the general public to ensure the responsible and equitable implementation of AI in healthcare.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT-MT: Connected Care System For Elderly Anxiety Management Via Music Therapy","authors":"Shama Siddiqui,&nbsp;Anwar Ahmed Khan,&nbsp;Muhammad Shoaib Siddiqui,&nbsp;Indrakshi Dey","doi":"10.1049/htl2.70016","DOIUrl":"10.1049/htl2.70016","url":null,"abstract":"<p>The elderly are at a higher risk of developing depression, anxiety, and other mental illnesses due to their age, chronic diseases, financial dependencies and other factors. Access to mental health services is often limited due to high cost, a low number of psychologists/therapists, and social taboos. In this paper, we propose automating the emerging technique of ‘music therapy’ (MT) using the internet of things (IoT) to develop a novel solution ‘IoT-MT’. IoT-MT has the potential to deal with psychological disorders in a convenient, quick, and cost-effective manner. The system is based on collecting body parameters (pulse rate, SPO_2, and blood pressure) from the patient demonstrating anxiety levels and playing appropriate music on a handheld device if the received parameters are above a certain predefined threshold. An early version of the system prototype is developed along with the mobile app, and experiments were conducted at a general hospital in Karachi, Pakistan, with 500 patients and their corresponding cardiologists. It has been observed that the proposed MT system reduced the anxiety levels for the majority of the patients, as indicated by their pulse rate and blood pressure. Also, the anxiety detection of IoT-MT was found to be accurate when compared to the patients’ self-assessment performed using the standard psychology tool, state-trait anxiety inventory (STAI).</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Next-Generation Human Activity Recognition Using Locality Constrained Linear Coding Combined With Machine Learning (NG-HAR-LCML)","authors":"Maryam Shabbir,&nbsp;Fahad Ahmad,&nbsp;Saad Awadh Alanazi,&nbsp;Muhammad Hassan Khan,&nbsp;Jianqiang Li,&nbsp;Tariq Mahmood,&nbsp;Shahid Naseem,&nbsp;Muhammad Anwar","doi":"10.1049/htl2.70013","DOIUrl":"10.1049/htl2.70013","url":null,"abstract":"<p>Accurate Human Activity Recognition (HAR) is a critical challenge with wide-ranging applications in healthcare, assistive technologies, and human-computer interaction. Traditional feature extraction methods often struggle to capture the complex spatial and temporal dynamics of human movements, leading to suboptimal classification performance. To address this limitation, this study introduces a novel encoding approach using Locality-Constrained Linear Coding (LLC) to enhance the discriminative power of hand-crafted features extracted from low-cost wearable sensors—an accelerometer and a gyroscope. The proposed LLC-based encoding scheme enables robust feature representation, improving the accuracy of HAR models. The encoded features are classified using a diverse set of Machine Learning (ML) and Deep Learning (DL) algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbours (KNN), AdaBoost, Gradient Boosting Machine (GBM), and Deep Belief Network (DBN). Extensive quantitative evaluations demonstrate that LLC significantly outperforms conventional feature encoding techniques, leading to improved classification accuracy. Among the tested models, DBN achieves a state-of-the-art accuracy of 99%, highlighting its superiority for HAR tasks. The contributions of this research are threefold: (1) it establishes the necessity of an advanced encoding scheme (LLC) for feature enhancement in HAR, (2) it provides a rigorous comparative analysis of multiple ML and DL classifiers, and (3) it introduces a scalable and cost-effective HAR framework suitable for real-world applications. Performance is comprehensively assessed using robust evaluation metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall, and Matthews Correlation Coefficient (MCC). The findings of this study offer new insights into feature encoding for HAR, setting a foundation for future advancements in sensor-based activity recognition.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Analysis of Monitoring Solutions for CAR T Cell Production","authors":"Arber Shoshi,&nbsp;Yuchen Xia,&nbsp;Andrea Fieschi,&nbsp;Yannick Baumgarten,&nbsp;Andrea Gaißler,&nbsp;Thomas Ackermann,&nbsp;Peter Reimann,&nbsp;Bernhard Mitschang,&nbsp;Michael Weyrich,&nbsp;Thomas Bauernhansl,&nbsp;Robert Miehe","doi":"10.1049/htl2.70012","DOIUrl":"10.1049/htl2.70012","url":null,"abstract":"<p>The chimeric antigen receptor T cell (CAR T) therapy has shown remarkable results in treating certain cancers. It involves genetically modifying a patient's T cells to recognize and attack cancer cells. Despite its potential, CAR T cell therapy is complex and costly and requires the integration of multiple technologies and specialized equipment. Further research is needed to achieve the maximum potential of CAR T cell therapies and to develop effective and efficient methods for their production. This paper presents an overview of current measurement methods used in the key steps of the production of CAR T cells. The study aims to assess the state of the art in monitoring solutions and identify their potential for online monitoring. The results of this paper contribute to the understanding of measurement methods in CAR T cell manufacturing and identify areas where on-line monitoring can be improved. Thus, this research facilitates progress toward the development of effective monitoring of CAR T cell therapies.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Excessive Muscle Co-Contraction on Sit-To-Stand Performance in High-Heeled Footwear","authors":"Ganesh R. Naik,&nbsp;Amit N. Pujari","doi":"10.1049/htl2.70011","DOIUrl":"10.1049/htl2.70011","url":null,"abstract":"<p>This study aimed to analyse the effects of co-contraction on quadriceps and hamstring muscles during sit-to-stand (STS) tasks for females wearing shoes with different heel heights. The study aimed to identify compensatory strategies during the STS tasks in response to excessive muscle co-contraction during high-heeled gait. Sixteen healthy young women (age: 24.4 ± 1.7 years, body mass index: 18.4 ± 1 kg/m², weight: 50.2 ± 5.2 kg, height: 1.63 ± 4.4 m) participated in this study. Electromyography signals were recorded from three quadriceps (vastus medialis, vastus lateralis, and rectus femoris) and one hamstring (semitendinosus) muscles. The participants wore shoes with different heights, including 4, 6, 8, and 10 cm. For each heel height, the co-contraction index is computed to measure postural balance using the quadriceps to hamstring muscle pairs. The results that were obtained and quantified with statistical measures show that for elevated shoes, if co-contraction increases, both quadriceps and hamstring muscles tend to compensate. This suggests that the capacity of the quadriceps and hamstring muscles to compensate is essential to retain normal walking and STS tasks in co-contracted persons. However, the compensation mechanisms may induce imbalance, muscle stiffness, and fatigue for women who regularly use high-heeled shoes during sit-to-stand tasks.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Self-Monitoring Mobile App to Mitigate Risk Factors for Suicide and Self-Harm in Junior (Resident) Doctors: A Review, Thematic Analysis and Concept Proposal","authors":"Kirsten Leslie,&nbsp;Chloe Sawyer,&nbsp;Katy Oak,&nbsp;Gareth Lewis,&nbsp;Bryan Clark,&nbsp;Anna Mankee-Williams,&nbsp;Ellen Wilkinson,&nbsp;Hiu Lam,&nbsp;Richard Laugharne,&nbsp;Rohit Shankar","doi":"10.1049/htl2.70009","DOIUrl":"10.1049/htl2.70009","url":null,"abstract":"<p>Doctors, particularly those in training in the UK, are exposed to high levels of stress in their work, which can lead to burnout and mental health problems. According to the health and safety executive (HSE) Management UK standards, employers should recognise and minimise work-related stress for staff. Our review looks to examine if known risk factors for suicide and self-harm in doctors align with the themes of the HSE management standards on stress control i.e., demand, control, support, relationships, role, and change and if so, could this be used to build a self-awareness digital application. Four research databases were searched using combinations of text words and thesaurus terms and predefined inclusion/exclusion criteria for relevant article retrieval. A thematic analysis was undertaken, aligning articles to their respective HSE standards. Twenty-six articles met the full inclusion criteria. 96.2% (25/26 papers) mentioned or aligned at least one of the HSE management standards, with 44% discussing three or more. Work-related risk factors for self-harm and suicide in doctors link well to the HSE management standards. We conceptualise a self-monitoring digital well-being tool for doctors to monitor stress.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Process Mining for Identifying the Critical Activities in Sepsis Trajectories","authors":"Mohsen Mohammadi","doi":"10.1049/htl2.70010","DOIUrl":"10.1049/htl2.70010","url":null,"abstract":"<p>Sepsis, a life-threatening condition with high mortality and readmission rates, demands precise and timely management to improve patient outcomes. Despite advancements, identifying the critical activities within sepsis treatment pathways remains a challenge, limiting the effectiveness of interventions. This study addresses this issue by utilising comparative process mining techniques to analyse sepsis trajectories, focusing on key performance metrics—sojourn time, arrival rate and finish rate—across distinct patient clusters. The analysis is based on real-life event logs from a hospital's sepsis cases, using K-means clustering to segment patients by age, severity and key clinical indicators. The study reveals critical activities such as ‘Return ER’, ‘Admission IC’, and ‘Release C’, which consistently exhibit high sojourn times and influence patient outcomes significantly. These activities emerge as bottlenecks in the patient care process, particularly in cases of severe sepsis, where delays can lead to increased complications and mortality. By identifying these critical points, the study provides actionable insights for healthcare providers to optimize resource allocation, reduce delays and enhance the overall efficiency of sepsis management. The findings underscore the importance of targeted interventions in these key areas, offering a path toward improved clinical outcomes and reduced sepsis-related mortality and readmission rates. This research contributes to the growing field of process mining in healthcare, highlighting its potential to transform complex clinical pathways into more efficient and effective treatment processes.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}