cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Adam Mudinillah
Contact Email
adammudinillah@staialhikmahpariangan.ac.id
Phone
+6285379388533
Journal Mail Official
adammudinillah@staialhikmahpariangan.ac.id
Editorial Address
Jln. Batu Tujuh Tapak, Jorong Sungai Tarab, Kec. Sungai Tarab Kab. Tanah Datar Prov. Sumatera Barat - Kode Pos: 27261
Location
Kab. tanah datar,
Sumatera barat
INDONESIA
Journal of World Future Medicine, Health and Nursing
Published by Yayasan Adra Karima Hubbi
ISSN : 29880459     EISSN : 29887550     DOI : 10.70177/health
Core Subject : Health,
Health Professions Medicine & Pharmacology Nursing Public Health Veterinary
Journal of World Future Medicine, Health and Nursing is a leading international journal focused on the global exchange of knowledge in medicine, health, and nursing, as well as advancing research and practice across health disciplines. The journal provides a forum for articles reporting on original research, systematic and scholarly reviews focused on health science, clinical practice and education from around the world. Journal of World Future Medicine, Health and Nursing publishes national and international research in an attempt to present a reliable and respectable information source for the researchers. Journal of World Future Medicine, Health and Nursing has been published since 2023, published three times a year January, May and September. The articles submitted for publication are subjected to double-blind reviewing process. The journal publishes original articles in English.
Arjuna Subject : Keperawatan - Keperawatan (Lain-Lain)
Articles 94 Documents
 6   7   8   9   10 
Technology Strategies in Health Promotion: Preventive Lifestyle Interventions to Reduce the Burden of Disease Ridwan, Eka Sari; Ahmad, Omar; Ali, Zainab
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i1.1905

Abstract

The global burden of disease, driven largely by preventable lifestyle factors such as poor diet, physical inactivity, and smoking, continues to strain healthcare systems worldwide. In response, health promotion strategies incorporating technological innovations have gained prominence as effective tools for preventive interventions. This study explores various technology-based strategies in health promotion, focusing on their role in encouraging preventive lifestyle changes to reduce the incidence of chronic diseases. The research employs a systematic review methodology, analyzing data from 40 peer-reviewed studies that evaluate the effectiveness of digital interventions such as mobile health apps, telemedicine, and wearable devices in promoting healthy behaviors. The findings indicate that technology-based interventions significantly improve health outcomes by increasing physical activity, enhancing dietary habits, and reducing smoking rates. Additionally, these interventions are shown to be highly effective in engaging populations that may have limited access to traditional healthcare services. The study concludes that technology-based health promotion strategies offer scalable, cost-effective solutions to reducing the burden of disease. However, challenges remain in ensuring equitable access and addressing concerns related to data privacy and security. The research underscores the importance of integrating technological tools into public health strategies to drive long-term improvements in population health.
Facing the Impact of Climate Change on Global Health: Science and Technology Based Adaptation Demir, Ahmet; Erdogan, Aylin; Musdania, Musdania; Zani, Benny Novico
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i1.1906

Abstract

Climate change poses a significant threat to global health, exacerbating existing health challenges and creating new risks. Rising temperatures, extreme weather events, and shifting disease patterns are already contributing to the increasing burden of diseases such as malaria, heatstroke, and respiratory disorders. This research explores the role of science and technology in adapting to the health impacts of climate change, focusing on innovative solutions to mitigate the health risks associated with environmental changes. The study employs a systematic review approach, analyzing data from 50 peer-reviewed studies that examine technological advancements, such as climate-resilient healthcare infrastructure, early warning systems, and the development of heat-resistant crops. The results indicate that technology-based adaptation strategies can significantly reduce the impact of climate change on public health by improving disease forecasting, enhancing healthcare system resilience, and supporting preventive measures. The study concludes that multi-disciplinary approaches involving science, technology, and policy-making are crucial to address the health challenges posed by climate change. Collaboration across sectors is needed to implement these strategies on a global scale, ensuring equitable access to climate-related health solutions. This research underscores the importance of continued investment in climate-resilient health systems to safeguard global health in the face of climate change.
Sustainable Health Model: Increasing Universal Access to Health Services in Remote Areas Ardenny, Ardenny; Nam, Le Hoang; Tu, Pham Anh
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i1.1907

Abstract

Access to healthcare services in remote areas remains a significant global challenge, with many populations experiencing disparities in healthcare availability, quality, and affordability. Sustainable health models that ensure universal access to health services are essential for improving public health outcomes in underserved areas. This study investigates the potential for sustainable health models to increase healthcare access in remote regions, focusing on the role of telemedicine, mobile health clinics, and community health workers. The research employs a mixed-methods approach, combining qualitative interviews with healthcare professionals and quantitative data on healthcare access and outcomes in remote communities. The findings indicate that telemedicine platforms have improved healthcare delivery by 40%, while mobile health clinics and trained community health workers have expanded service reach, particularly in geographically isolated areas. Furthermore, community-based health interventions have led to a 30% reduction in preventable diseases in these regions. The study concludes that integrating technology with community-based solutions offers a scalable and effective approach to achieving universal health access in remote areas. However, challenges such as technology infrastructure, resource allocation, and healthcare workforce training need to be addressed to ensure the sustainability of these models.
AI-Driven Diagnostic Imaging: Enhancing Early Cancer Detection through Deep Learning Models Ariyanto, Danang; Chai, Napat; Krit, Pong
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 2 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i3.2369

Abstract

Early detection is critical for improving cancer survival rates, yet the interpretation of diagnostic images is subject to human error and variability. Artificial intelligence (AI), specifically deep learning, presents a transformative opportunity to enhance diagnostic accuracy and speed. This study aimed to develop and validate a deep learning model to improve the accuracy and efficiency of early-stage cancer detection in radiological images compared to human expert interpretation. A convolutional neural network (CNN) was trained and validated on a curated dataset of over 20,000 mammography images. The model's diagnostic performance was rigorously evaluated using key metrics, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC), against a biopsy-verified ground truth. The AI model achieved an overall accuracy of 97.2%, with a sensitivity of 98.1% and a specificity of 96.5%. The model's performance, with an AUC of 0.98, was comparable to that of senior radiologists and significantly reduced false-negative rates. AI-driven deep learning models are highly effective and reliable tools for augmenting diagnostic imaging. They can significantly enhance early cancer detection, reduce diagnostic errors, and serve as a powerful assistive tool for radiologists in clinical practice.

Page 10 of 10 | Total Record : 94

 6   7   8   9   10 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 3 (2025) Vol. 3 No. 2 (2025) Vol. 3 No. 1 (2025) Vol. 2 No. 3 (2024) Vol. 2 No. 2 (2024) Vol. 2 No. 1 (2024) Vol. 1 No. 3 (2023) Vol. 1 No. 2 (2023) Vol. 1 No. 1 (2023)