Health Care Science最新文献

{"title":"Leveraging anatomical constraints with uncertainty for pneumothorax segmentation","authors":"Han Yuan,&nbsp;Chuan Hong,&nbsp;Nguyen Tuan Anh Tran,&nbsp;Xinxing Xu,&nbsp;Nan Liu","doi":"10.1002/hcs2.119","DOIUrl":"10.1002/hcs2.119","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Pneumothorax is a medical emergency caused by the abnormal accumulation of air in the pleural space—the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum, and we refer to this area as “lung + space.” While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach. These models directly map chest radiographs to clinician-annotated lesion areas, often neglecting the vital domain knowledge that pneumothorax is inherently location-sensitive.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We propose a novel approach that incorporates the lung + space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs. To circumvent the need for additional annotations and to prevent potential label leakage on the target task, our method utilizes external datasets and an auxiliary task of lung segmentation. This approach generates a specific constraint of lung + space for each chest radiograph. Furthermore, we have incorporated a discriminator to eliminate unreliable constraints caused by the domain shift between the auxiliary and target datasets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our results demonstrated considerable improvements, with average performance gains of 4.6%, 3.6%, and 3.3% regarding intersection over union, dice similarity coefficient, and Hausdorff distance. These results were consistent across six baseline models built on three architectures (U-Net, LinkNet, or PSPNet) and two backbones (VGG-11 or MobileOne-S0). We further conducted an ablation study to evaluate the contribution of each component in the proposed method and undertook several robustness studies on hyper-parameter selection to validate the stability of our method.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The integration of domain knowledge in DL models for medical applications has often been underemphasized. Our research underscores the significance of incorporating medical domain knowledge about the location-specific nature of pneumothorax to enhance DL-based lesion segmentation and further bolster clinicians' trust in DL tools. Beyond pneumothorax, our approach is promising for other thoracic conditions that possess location-relevant characteristics.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 6","pages":"456-474"},"PeriodicalIF":0.0,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904715","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 novel ensemble ARIMA-LSTM approach for evaluating COVID-19 cases and future outbreak preparedness","authors":"Somit Jain,&nbsp;Shobhit Agrawal,&nbsp;Eshaan Mohapatra,&nbsp;Kathiravan Srinivasan","doi":"10.1002/hcs2.123","DOIUrl":"10.1002/hcs2.123","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The global impact of the highly contagious COVID-19 virus has created unprecedented challenges, significantly impacting public health and economies worldwide. This research article conducts a time series analysis of COVID-19 data across various countries, including India, Brazil, Russia, and the United States, with a particular emphasis on total confirmed cases.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The proposed approach combines auto-regressive integrated moving average (ARIMA)'s ability to capture linear trends and seasonality with long short-term memory (LSTM) networks, which are designed to learn complex nonlinear dependencies in the data. This hybrid approach surpasses both individual models and existing ARIMA-artificial neural network (ANN) hybrids, which often struggle with highly nonlinear time series like COVID-19 data. By integrating ARIMA and LSTM, the model aims to achieve superior forecasting accuracy compared to baseline models, including ARIMA, Gated Recurrent Unit (GRU), LSTM, and Prophet.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The hybrid ARIMA-LSTM model outperformed the benchmark models, achieving a mean absolute percentage error (MAPE) score of 2.4%. Among the benchmark models, GRU performed the best with a MAPE score of 2.9%, followed by LSTM with a score of 3.6%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proposed ARIMA-LSTM hybrid model outperforms ARIMA, GRU, LSTM, Prophet, and the ARIMA-ANN hybrid model when evaluating using metrics like MAPE, symmetric mean absolute percentage error, and median absolute percentage error across all countries analyzed. These findings have the potential to significantly improve preparedness and response efforts by public health authorities, allowing for more efficient resource allocation and targeted interventions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 6","pages":"409-425"},"PeriodicalIF":0.0,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904696","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":"Explainable machine learning model for pre-frailty risk assessment in community-dwelling older adults","authors":"Chenlin Du,&nbsp;Zeyu Zhang,&nbsp;Baoqin Liu,&nbsp;Zijian Cao,&nbsp;Nan Jiang,&nbsp;Zongjiu Zhang","doi":"10.1002/hcs2.120","DOIUrl":"10.1002/hcs2.120","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Frailty in older adults is linked to increased risks and lower quality of life. Pre-frailty, a condition preceding frailty, is intervenable, but its determinants and assessment are challenging. This study aims to develop and validate an explainable machine learning model for pre-frailty risk assessment among community-dwelling older adults.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The study included 3141 adults aged 60 or above from the China Health and Retirement Longitudinal Study. Pre-frailty was characterized by one or two criteria from the physical frailty phenotype scale. We extracted 80 distinct features across seven dimensions to evaluate pre-frailty risk. A model was constructed using recursive feature elimination and a stacking-CatBoost distillation module on 80% of the sample and validated on a separate 20% holdout data set.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The study used data from 2508 community-dwelling older adults (mean age, 67.24 years [range, 60–96]; 1215 [48.44%] females) to develop a pre-frailty risk assessment model. We selected 57 predictive features and built a distilled CatBoost model, which achieved the highest discrimination (AUROC: 0.7560 [95% CI: 0.7169, 0.7928]) on the 20% holdout data set. The living city, BMI, and peak expiratory flow (PEF) were the three most significant contributors to pre-frailty risk. Physical and environmental factors were the top 2 impactful feature dimensions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>An accurate and interpretable pre-frailty risk assessment framework using state-of-the-art machine learning techniques and explanation methods has been developed. Our framework incorporates a wide range of features and determinants, allowing for a comprehensive and nuanced understanding of pre-frailty risk.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 6","pages":"426-437"},"PeriodicalIF":0.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904707","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":"SkinSage XAI: An explainable deep learning solution for skin lesion diagnosis","authors":"Geetika Munjal,&nbsp;Paarth Bhardwaj,&nbsp;Vaibhav Bhargava,&nbsp;Shivendra Singh,&nbsp;Nimish Nagpal","doi":"10.1002/hcs2.121","DOIUrl":"10.1002/hcs2.121","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Skin cancer poses a significant global health threat, with early detection being essential for successful treatment. While deep learning algorithms have greatly enhanced the categorization of skin lesions, the black-box nature of many models limits interpretability, posing challenges for dermatologists.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To address these limitations, SkinSage XAI utilizes advanced explainable artificial intelligence (XAI) techniques for skin lesion categorization. A data set of around 50,000 images from the Customized HAM10000, selected for diversity, serves as the foundation. The Inception v3 model is used for classification, supported by gradient-weighted class activation mapping and local interpretable model-agnostic explanations algorithms, which provide clear visual explanations for model outputs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>SkinSage XAI demonstrated high performance, accurately categorizing seven types of skin lesions—dermatofibroma, benign keratosis, melanocytic nevus, vascular lesion, actinic keratosis, basal cell carcinoma, and melanoma. It achieved an accuracy of 96%, with precision at 96.42%, recall at 96.28%, <span><i>F</i>₁</span> score at 96.14%, and an area under the curve of 99.83%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>SkinSage XAI represents a significant advancement in dermatology and artificial intelligence by bridging gaps in accuracy and explainability. The system provides transparent, accurate diagnoses, improving decision-making for dermatologists and potentially enhancing patient outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 6","pages":"438-455"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904685","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":"Are private hospital emergency departments in Australia distributed to serve the wealthy community?","authors":"Mazen Baazeem,&nbsp;Estie Kruger,&nbsp;Marc Tennant","doi":"10.1002/hcs2.112","DOIUrl":"10.1002/hcs2.112","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study investigates the geographical distribution of private hospitals in Australian capital cities in relation to the Index of Relative Socioeconomic Disadvantage.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using Geographic Information System analysis, the study examined how private hospitals are distributed across different socioeconomic quartiles, providing a comprehensive visualisation of health care accessibility.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The results indicate an unequal distribution with a substantial concentration of private hospitals within the vicinity of communities classified in the highest socioeconomic classification. This raises significant concerns about health care equity, particularly in light of the increased strain on health care systems before, during and after the COVID-19 pandemic.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study underscores the need for targeted policy interventions to enhance the resilience and accessibility of the private health care sector, specifically targeting disadvantaged communities. It suggests that comprehensive, geographically-informed data is crucial for policymakers to make informed decisions that promote health equity in the postpandemic landscape.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 5","pages":"287-297"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520243/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549985","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":"Advancing digital health in China: Aligning challenges, opportunities, and solutions with the Global Initiative on Digital Health (GIDH)","authors":"Ge Wu,&nbsp;Mengchun Gong,&nbsp;You Wu,&nbsp;Li Liu,&nbsp;Boyang Shi,&nbsp;Zhirong Zeng","doi":"10.1002/hcs2.118","DOIUrl":"10.1002/hcs2.118","url":null,"abstract":"<p>We summarized the unique challenges that China faced in digital health due to its large population, regional disparities, and uneven distribution of medical resources. Under the guidance of the Global Initiative on Digital Health (GIDH) released by WHO, we proposed corresponding solutions that address infrastructure, data, terminology, technology and security.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 5","pages":"365-369"},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549974","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":"Toward real-world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials","authors":"Han Yuan","doi":"10.1002/hcs2.114","DOIUrl":"10.1002/hcs2.114","url":null,"abstract":"<p>In this commentary, we elucidate three indispensable evaluation steps toward the real-world deployment of machine learning within the healthcare sector and demonstrate referable examples for diagnostic, therapeutic, and prognostic tasks. We encourage researchers to move beyond retrospective and within-sample validation, and step into the practical implementation at the bedside rather than leaving developed machine learning models in the dust of archived literature.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 5","pages":"360-364"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549989","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":"Improving transitional care after acute myocardial infarction: A scoping review","authors":"Filipa Homem,&nbsp;Anaísa Reveles,&nbsp;António Amaral,&nbsp;Verónica Coutinho,&nbsp;Lino Gonçalves","doi":"10.1002/hcs2.116","DOIUrl":"10.1002/hcs2.116","url":null,"abstract":"<p>Cardiovascular disease remains the leading cause of morbidity and mortality, posing a significant challenge to healthcare systems worldwide. Transitional care interventions, which ensure coordination and continuity of care as patients move between different levels of healthcare, have been shown to reduce unnecessary healthcare utilization and improve patient outcomes. While much attention has been given to transitional care in heart failure, this review aims to map the interventions implemented for patients following an acute myocardial infarction (AMI). A scoping review was conducted following the Joanna Briggs Institute (JBI) methodology, with literature searches performed in the Cochrane, CINAHL, MEDLINE, JBI, and SciELO databases, focusing on publications from 2013 onwards in both Portuguese and English. Seventy-five studies were included, with most combining multiple interventions that contributed to improved cardiovascular health outcomes, including increased adherence to healthy lifestyle behaviors, enhanced medication compliance, and better healthcare self-management. These interventions were effective in reducing cardiovascular-related Emergency Department visits, unplanned 30-day readmissions, and mortality following a first-time myocardial infarction. Key strategies identified included discharge planning, digital health solutions, outpatient care, and healthcare coordination. The findings of this review underscore the need for developing methodologies that enhance the transition of care from hospital to primary care following an AMI. There is an urgent need to design and implement new healthcare programs that integrate discharge interventions, digital health, outpatient care, and healthcare coordination to ensure continuity of care and optimize patient outcomes post-discharge.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 5","pages":"312-328"},"PeriodicalIF":0.0,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142550105","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 African Medicines Agency and Medicines Regulation: Progress, challenges, and recommendations","authors":"Adeniyi Ayinde Abdulwahab,&nbsp;Ukamaka Gladys Okafor,&nbsp;Damilola Samuel Adesuyi,&nbsp;Adriana Viola Miranda,&nbsp;Rashidat Onyinoyi Yusuf,&nbsp;Don Eliseo Lucero-Prisno III","doi":"10.1002/hcs2.117","DOIUrl":"10.1002/hcs2.117","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 <p>In response to the situation of the African healthcare system, the African Medicines Agency (AMA) was established by the African Union (AU) to regulate access to medicines and support the local manufacture of medications. This study aimed to describe the factors that enabled the establishment of the African Medicines Agency and its successes, challenges, and perceived benefits. We reviewed data sources that explored the progress and challenges of the African Medicines Agency and Medicines Regulation in Africa. The SPIDER framework was used to organise the research focus and to extract the keywords for the literature search. The study data were obtained from PubMed Central, ScienceDirect, and Google Scholar. Out of 249 studies screened, 19 were selected for this narrative review. Critical successes observed in the agency's establishment include the appointment of a Special Envoy, the selection of its headquarters, and the signing of its treaty by 37 member states. However, it is hindered by poor political commitment, differences in risk-benefits interpretation and organizational structure, weak legal and regulatory frameworks, inadequate financial mechanisms, and inadequate political and policy leadership in some member states. The value of AMA in achieving optimal health outcomes and its other benefits must be considered despite the challenges being encountered. Therefore, all member states should adopt the best procedures in signing and ratifying the treaty and implementing associated commitments to improve efficiency and accountability in African medicine regulation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 5","pages":"350-359"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549988","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":"Revolutionizing healthcare and medicine: The impact of modern technologies for a healthier future—A comprehensive review","authors":"Aswin Thacharodi,&nbsp;Prabhakar Singh,&nbsp;Ramu Meenatchi,&nbsp;Z. H. Tawfeeq Ahmed,&nbsp;Rejith R. S. Kumar,&nbsp;Neha V,&nbsp;Sanjana Kavish,&nbsp;Mohsin Maqbool,&nbsp;Saqib Hassan","doi":"10.1002/hcs2.115","DOIUrl":"10.1002/hcs2.115","url":null,"abstract":"<p>The increasing integration of new technologies is driving a fundamental revolution in the healthcare sector. Developments in artificial intelligence (AI), machine learning, and big data analytics have completely transformed the diagnosis, treatment, and care of patients. AI-powered solutions are enhancing the efficiency and accuracy of healthcare delivery by demonstrating exceptional skills in personalized medicine, early disease detection, and predictive analytics. Furthermore, telemedicine and remote patient monitoring systems have overcome geographical constraints, offering easy and accessible healthcare services, particularly in underserved areas. Wearable technology, the Internet of Medical Things, and sensor technologies have empowered individuals to take an active role in tracking and managing their health. These devices facilitate real-time data collection, enabling preventive and personalized care. Additionally, the development of 3D printing technology has revolutionized the medical field by enabling the production of customized prosthetics, implants, and anatomical models, significantly impacting surgical planning and treatment strategies. Accepting these advancements holds the potential to create a more patient-centered, efficient healthcare system that emphasizes individualized care, preventive care, and better overall health outcomes. This review's novelty lies in exploring how these technologies are radically transforming the healthcare industry, paving the way for a more personalized and effective healthcare for all. It highlights the capacity of modern technology to revolutionize healthcare delivery by addressing long-standing challenges and improving health outcomes. Although the approval and use of digital technology and advanced data analysis face scientific and regulatory obstacles, they have the potential for transforming translational research. as these technologies continue to evolve, they are poised to significantly alter the healthcare environment, offering a more sustainable, efficient, and accessible healthcare ecosystem for future generations. Innovation across multiple fronts will shape the future of advanced healthcare technology, revolutionizing the provision of healthcare, enhancing patient outcomes, and equipping both patients and healthcare professionals with the tools to make better decisions and receive personalized treatment. As these technologies continue to develop and become integrated into standard healthcare practices, the future of healthcare will probably be more accessible, effective, and efficient than ever before.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 5","pages":"329-349"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520245/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549987","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}