Journal of World Future Medicine, Health and Nursing

Gamification in Mobile Health Apps: A Quasi-Experimental Study on Adolescent Obesity Prevention

Cau Kim Jiu, Ming Kiri, Sokha Dara — Sat, 30 Aug 2025 00:00:00 +0700

Adolescent obesity has become a global public health crisis, demanding effective and engaging intervention strategies. While mobile health (mHealth) apps offer a scalable platform for promoting healthy lifestyles, maintaining long-term engagement among adolescents remains a significant challenge. This study aimed to evaluate the effectiveness of a gamified Health application on promoting positive changes in physical activity, dietary habits, and anthropometric measures among adolescents at risk for obesity, compared to a non-gamified version of the same application. A 12-week, quasi-experimental study was conducted with 150 adolescents (aged 13-16) with a BMI above the 85th percentile. Participants were assigned to either an intervention group (n=75) using a gamified mHealth app featuring points, badges, and leaderboards, or a control group (n=75) using a non-gamified version with identical health content. The intervention group demonstrated significantly greater increases in moderate-to-vigorous physical activity (p < .01) and higher consumption of fruits and vegetables (p < .05) compared to the control group. Furthermore, the gamified app users showed a modest but statistically significant reduction in their BMI z-score (p < .05), a change not observed in the control group. App engagement metrics were also 70% higher in the intervention group. The integration of gamification elements into mHealth applications is a highly effective strategy for preventing obesity in adolescents.

Heatwaves and Urban Elderly Mortality: A Retrospective Cohort Study Using Remote Sensing Data

Sri Suparni, Clara Mendes, Bruna Costa — Sat, 30 Aug 2025 00:00:00 +0700

Climate change is intensifying the frequency and severity of heatwaves, posing a significant threat to public health. Urban elderly populations are particularly vulnerable due to the urban heat island effect and age-related physiological sensitivities. Quantifying this risk with precision is essential for developing targeted public health interventions. This study aimed to quantify the association between exposure to extreme heat events, as measured by remote sensing data, and all-cause mortality among an elderly urban population. A retrospective cohort study was conducted using health data for 50,000 urban residents aged 65 and over from 2015-2022. Land Surface Temperature (LST) data derived from Landsat satellites were used to define heatwave exposure at a granular, neighborhood level. Cox proportional hazards models were used to analyze the association between heatwave exposure and mortality, adjusting for confounding variables. A significant association was found between heatwave exposure and increased mortality risk. For each 1°C increase in LST during a heatwave, there was a 5.2% (95% CI: 4.5%-6.0%) increase in all-cause mortality. The risk was most pronounced in neighborhoods with lower green space coverage. Satellite-derived remote sensing data provide a powerful tool for assessing heatwave-related mortality risk in urban elderly populations. These findings underscore the urgent need for urban planning and public health strategies focused on heat mitigation to protect vulnerable residents.

School-Based Mental Health Interventions: A Systematic Review of Digital CBT Programs for Teens

Omar Khan, Clara Mendes, Marcus Tan, Ardi Azhar Nampira — Sat, 30 Aug 2025 00:00:00 +0700

Social isolation is a significant and modifiable risk factor for accelerated cognitive decline and dementia in aging populations. Traditional methods for detecting cognitive changes, such as clinical screenings, are often infrequent and fail to capture the subtle, early behavioral shifts that precede a formal diagnosis. This study aimed to develop and validate an artificial intelligence model designed for the early detection of cognitive decline by passively monitoring behavioral and vocal biomarkers of social isolation in older adults living independently. A 24-month, prospective longitudinal study was conducted with a cohort of 200 community-dwelling adults aged 70 and older. A suite of unobtrusive in-home sensors was used to passively collect data on movement patterns, social communication (frequency and duration of conversations), and computer/phone usage. The AI-powered system identified individuals who would later show clinically significant cognitive decline with an accuracy of 91% and a lead time of approximately 7 months before formal assessment. The model successfully distinguished between simple loneliness and the specific behavioral patterns of social withdrawal associated with cognitive impairment. AI-powered passive monitoring systems are a highly effective and ecologically valid tool for the pre-clinical detection of cognitive decline linked to social isolation.

Robotics in Surgery: Enhancing Precision, Safety, and Patient Outcomes

Anggun Wida Prawira, Vindi Tyastutik — Wed, 12 Nov 2025 00:00:00 +0700

Robotic-assisted surgery has revolutionized modern surgical practice by enhancing precision, reducing invasiveness, and improving patient outcomes. Traditional surgical techniques are often limited by human dexterity, fatigue, and the complexity of intricate procedures, which can increase the risk of complications and prolong recovery. Robotics offers advanced visualization, tremor filtration, and enhanced instrument control, enabling surgeons to perform complex procedures with greater accuracy and consistency. This study investigates the impact of robotic-assisted surgery on surgical precision, safety, and patient outcomes across multiple specialties. A systematic review and meta-analysis were conducted, synthesizing data from clinical trials, observational studies, and surgical outcome reports. Metrics analyzed included intraoperative precision, complication rates, operative time, postoperative recovery, and patient satisfaction. Results indicate that robotic surgery significantly reduces intraoperative errors, minimizes blood loss, shortens hospital stays, and enhances functional recovery compared to conventional techniques. Surgeons reported improved ergonomics and operative control, contributing to procedural consistency and reduced fatigue. The study concludes that robotics in surgery represents a critical advancement in surgical care, offering tangible benefits for both patients and clinicians. Successful integration requires continued training, technological refinement, and assessment of cost-effectiveness to ensure sustainable adoption. These findings support the expansion of robotic-assisted interventions as a standard of care in complex surgical procedures.

Professional Relationships Can Improve the Quality of Nursing Services at Wijaya Kusumah Kuningan Hospital, West Java, Indonesia

Aria Pranatha, Idris Adewale Ahmed, Nabillah G L — Wed, 26 Nov 2025 00:00:00 +0700

Data from the Indonesian Ministry of Health (2020) shows that only 20% of health facilities measure service quality, with indicators such as patient identification compliance (66.67%) and emergency room response time (10.5%) still low. Professional relationship issues such as communication and collaboration between professionals also affect service quality, so that improvement efforts are needed to achieve quality services and are oriented towards patient satisfaction. The purpose of this study was to determine the relationship between professional relationships and the quality of nursing services. Methods: This study used a quantitative research method using a cross-sectional model. The population in this study were 58 nurses at Wijaya Kusumah Hospital, Kuningan, with a total sampling technique. The instruments used in this study were observation sheets and questionnaires. Data analysis was carried out according to univariate and bivariate using the Spearman Rank statistical test. Results: This study shows that most of the Professional Relationships were 47 people (81.0%), and of the 58 respondents, the quality of nursing services was mostly good (55 respondents (94.8%). The results of the Spearmen Rank test obtained a p value of 0.000 (<0.05) with an R value of 0.509. Conclusion: There is a significant relationship between Professional Relationship and the quality of nursing services in nursing services which include activities such as passes, nursing conferences, and nursing rounds, so that it can improve the nursing services provided to patients and strengthen the work team.

Artificial Intelligence in Precision Medicine: Transforming Genetic-Based Diagnostics and Patient Care

Fitriah Handayani, Nurul Huda, Benny Novico Zani, Muntasir Muntasir — Wed, 26 Nov 2025 00:00:00 +0700

Precision medicine aims to tailor healthcare strategies to individual genetic, environmental, and lifestyle factors, enhancing diagnostic accuracy and treatment efficacy. Traditional approaches to genetic-based diagnostics often face challenges such as high complexity, large-scale data interpretation, and time-intensive analyses. Artificial intelligence (AI) offers transformative potential by enabling rapid, data-driven analysis of genomic information, supporting personalized patient care, and improving clinical decision-making. This study investigates the role of AI in enhancing genetic-based diagnostics and patient care within precision medicine. A systematic review and critical analysis were conducted, integrating findings from peer-reviewed research, clinical reports, and AI-based diagnostic applications. The methodology focused on evaluating AI algorithms for genetic variant detection, risk prediction, and therapeutic recommendations, as well as assessing their clinical integration and outcomes. Results indicate that AI significantly improves the speed, accuracy, and interpretability of genomic analyses, facilitating early disease detection, individualized treatment planning, and predictive risk assessment. Challenges include data privacy, algorithmic transparency, and the need for robust validation in diverse populations. The study concludes that AI integration in precision medicine represents a pivotal advancement in genetic diagnostics and patient-centered care, offering scalable solutions for complex healthcare challenges. Ethical, regulatory, and technical considerations are essential to ensure safe, equitable, and effective implementation in clinical practice.

AI and Robotics in Elderly Care: Sustainable Solutions for Aging Populations

Thika Marliana, Dilara Sert Kasim, Samsuni Samsuni, Ton Kiat — Wed, 26 Nov 2025 00:00:00 +0700

The global rise in aging populations poses urgent challenges to healthcare systems, social welfare, and labor sustainability. Traditional caregiving models are increasingly strained by workforce shortages and escalating medical costs. Artificial Intelligence (AI) and robotics have emerged as transformative technologies offering innovative and sustainable approaches to elderly care. This study aims to examine how AI-driven systems and assistive robots enhance healthcare delivery, autonomy, and quality of life among older adults. Using a mixed-method design, the research combines a systematic review of 120 peer-reviewed studies (2012–2024) with case analyses of robotic implementations in Japan, Sweden, and Singapore. Findings reveal that AI-enabled monitoring, predictive diagnostics, and social robots significantly improve health outcomes, emotional well-being, and caregiving efficiency. However, ethical concerns regarding privacy, human empathy, and digital inequality remain critical barriers to widespread adoption. The study concludes that sustainable elderly care requires integrating technological innovation with human-centered design and policy frameworks that ensure inclusivity, accountability, and data ethics.

Tele-Nursing in Post-Operative Care: Expanding Accessibility and Reducing Readmission Ratesh

Catur Budi Susilo, Siri Lek, Pong Krit — Mon, 22 Dec 2025 00:00:00 +0700

Post-operative care is critical for ensuring optimal recovery, preventing complications, and reducing hospital readmission rates. Traditional follow-up methods often face limitations, including geographic barriers, limited access to healthcare providers, and resource constraints, which can compromise patient outcomes. Tele-nursing offers a promising solution by delivering remote monitoring, education, and guidance, thereby enhancing accessibility and supporting continuity of care. This study investigates the effectiveness of tele-nursing interventions in post-operative care, focusing on patient outcomes, adherence to care protocols, and readmission rates. A mixed-methods approach was employed, integrating quantitative analysis of clinical metrics and readmission data with qualitative assessments of patient and nurse experiences. Data were collected from 180 post-operative patients across multiple surgical departments who received tele-nursing support over a 90-day period. Results indicated that tele-nursing significantly reduced 30-day readmission rates, improved adherence to post-operative care instructions, and increased patient satisfaction. Nurses reported enhanced ability to monitor patient recovery, provide timely interventions, and offer personalized education remotely. The study concludes that tele-nursing is an effective strategy to expand access to post-operative care, improve patient outcomes, and reduce healthcare system burden. Implementation of structured tele-nursing protocols and integration with conventional care pathways can optimize benefits while ensuring patient safety and continuity of care.

Telemedicine for Rural Healthcare Access: Evaluating Effectiveness in Reducing Health Disparities

Safiullah Aziz, Jamil Khan, Razia Khan, Agus Darmawan — Mon, 22 Dec 2025 00:00:00 +0700

Rural populations face persistent barriers to healthcare access due to geographical isolation, workforce shortages, and limited infrastructure. Telemedicine has emerged as a promising strategy to bridge these gaps by enabling remote consultations, diagnosis, and follow-up care. Despite its increasing adoption, the effectiveness of telemedicine in reducing health disparities between rural and urban communities remains insufficiently evaluated. This study aims to assess the impact of telemedicine interventions on healthcare accessibility, patient outcomes, and equity in rural settings. A mixed-method approach was employed, combining quantitative analysis of service utilization data from 10 rural hospitals with qualitative interviews involving 85 healthcare professionals and patients. The findings demonstrate a 35% increase in patient access to primary and specialist care, alongside a significant reduction in appointment wait times and emergency referrals. Qualitative insights indicate enhanced patient satisfaction and continuity of care, though infrastructural and digital literacy barriers persist. The study concludes that telemedicine substantially improves healthcare equity in rural areas but requires sustained investment in technology, training, and policy integration to ensure long-term scalability and inclusivity. The results contribute to evidence-based policy frameworks for digital health expansion in underserved regions.

Nanomedicine in Cancer Therapy: Opportunities, Challenges, and Future Directions

Felipe Souza, Bruna Costa, Rafaela Lima, Dito Anurogo — Mon, 22 Dec 2025 00:00:00 +0700

Cancer remains one of the leading causes of mortality worldwide, demanding innovative therapeutic strategies that enhance efficacy while minimizing systemic toxicity. Nanomedicine, integrating nanotechnology with pharmacology, offers transformative potential for precision drug delivery, early diagnosis, and targeted therapy in oncology. This study aims to explore the opportunities, challenges, and future directions of nanomedicine in cancer treatment through a comprehensive analysis of recent scientific advancements and clinical trials. A systematic review method was employed, synthesizing data from 130 peer-reviewed studies published between 2015 and 2024. The findings reveal significant improvements in drug bioavailability, tumor-specific accumulation, and controlled release using nanoparticle-based platforms such as liposomes, dendrimers, and polymeric micelles. However, translational barriers persist, including biocompatibility issues, large-scale manufacturing limitations, and regulatory uncertainty. The study concludes that while nanomedicine has demonstrated remarkable therapeutic potential, its clinical integration requires multidisciplinary collaboration and standardized evaluation frameworks to ensure safety, scalability, and cost-effectiveness. The insights contribute to a forward-looking perspective on developing next-generation nanotherapeutics capable of reshaping cancer care through personalized, minimally invasive, and efficient interventions.

Preventive Lifestyle Interventions for Non-Communicable Diseases: Community-Based Innovations

Miftahul Jannah, Chen Mei, Asriana Abdullah, Yang Xiang — Mon, 22 Dec 2025 00:00:00 +0700

Non-communicable diseases (NCDs) remain a leading global health challenge, disproportionately affecting low- and middle-income countries and placing substantial burdens on healthcare systems and communities. Increasing evidence shows that preventive lifestyle interventions embedded in community settings can mitigate behavioral risk factors, yet empirical analyses of innovative community-based models remain limited. This study aims to examine the effectiveness of community-driven preventive lifestyle interventions in reducing modifiable NCD risk indicators and strengthening local health resilience. A mixed-methods design was employed, integrating quantitative assessment of biometric and behavioral indicators with qualitative exploration of community participation, program acceptability, and perceived benefits. Findings demonstrate that structured community interventions—such as peer-led education, culturally adapted physical activity programs, and neighborhood health monitoring—resulted in significant improvements in dietary behavior, physical activity levels, and blood pressure profiles across participating groups. Qualitative evidence further indicates increased health awareness, enhanced social support networks, and stronger community ownership of preventive health practices. The study concludes that community-based innovations play a critical role in promoting sustainable lifestyle modification and offer scalable models for NCD prevention, particularly in resource-constrained settings. Strengthening institutional support and integrating community-led approaches into broader public health policy are essential for maximizing long-term impact.

Community-Based Health Promotion Programs: Innovations for Sustainable Preventive Care

Rini Ambarwati, Minarti Minarti, Nur Hasanah, Benny Novico Zani — Mon, 22 Dec 2025 00:00:00 +0700

Growing concern over rising rates of preventable diseases and disparities in access to healthcare has intensified the need for innovative community-based health promotion models that support long-term preventive care. Sustainable preventive strategies increasingly rely on community engagement, participatory education, and context-sensitive interventions capable of addressing social, behavioral, and environmental determinants of health. This study aims to examine how innovative approaches within community-based health promotion programs can strengthen preventive practices and enhance community resilience. A mixed-methods design was employed, integrating survey data from program participants, in-depth interviews with community health workers, and field observations conducted across three urban and rural intervention sites. The findings reveal that community-driven innovations—such as culturally tailored health education, peer-led initiatives, and locally adapted preventive tools—significantly improve health literacy, encourage sustained behavioral change, and expand access to preventive resources. The programs demonstrated enhanced community ownership, stronger intersectoral collaboration, and measurable improvements in early detection and risk reduction behaviors. The study concludes that community-based health promotion programs, when supported by innovative and participatory frameworks, serve as an effective pathway toward sustainable preventive care. Strengthening adaptability, community partnership, and local capacity building emerges as essential for scaling and institutionalizing preventive health innovations.

Evidence-Based Palliative Care: Innovations in Pain Management and Quality of Life Improvement

Daiki Nishida, Miku Fujita, Zulkahfi Zulkahfi, Misroh Mulianingsih — Mon, 22 Dec 2025 00:00:00 +0700

Palliative care has undergone significant transformation as global health systems respond to increasing rates of chronic and life-limiting illnesses. Persistent challenges in pain management, symptom control, and psychosocial support highlight the nemerging evidence-based approaches in palliative pain management and evaluate their impact on improving holistic well-being for patients receiving palliative care. A systematic review methodology weed for evidence-based innovations that enhance patient quality of life. This study aims to examine as employed, synthesizing findings from clinical trials, observational studies, and multidisciplinary intervention reports published over the past decade. The results indicate that multimodal pain management strategies—combining pharmacological optimization, non-pharmacological therapies, and digital monitoring technologies—significantly reduce pain severity and enhance patient comfort. The evidence further demonstrates that integrative interventions, such as mindfulness-based therapies, personalized care planning, and family-centered support models, contribute meaningfully to psychological resilience and perceived quality of life. The study concludes that evidence-based innovations in palliative care provide measurable benefits across physical, emotional, and social dimensions, underscoring the importance of adopting interdisciplinary and patient-centered approaches. Continued refinement of these strategies is essential for meeting the growing and complex needs of palliative care populations.

Climate Change and Human Health: Strategies for Building Resilient Healthcare Systems

Irene Florensia Situmeang, Samantha Gonzales, Luis Santos — Mon, 22 Dec 2025 00:00:00 +0700

Climate change has intensified environmental stressors that directly and indirectly threaten human health, including rising temperatures, extreme weather events, vector-borne disease expansion, and disruptions to food and water security. Healthcare systems worldwide face increasing pressure as climate-related hazards exacerbate morbidity and mortality, revealing systemic vulnerabilities in infrastructure, workforce capacity, and emergency preparedness. This study aims to identify strategic approaches for strengthening healthcare system resilience in the face of climate change by examining adaptive, organizational, and technological interventions. A mixed-methods design was employed, combining systematic literature analysis with case-based evaluations of climate adaptation initiatives implemented across multiple regions. The findings show that integrated climate-health surveillance, climate-resilient infrastructure, workforce training, and digital early-warning systems are among the most effective strategies for enhancing resilience. The results also highlight that governance coordination and equitable resource allocation significantly shape adaptive capacity. The study concludes that building resilient healthcare systems requires a multisectoral approach that embeds climate risk assessment into health planning, expands adaptive technologies, and prioritizes vulnerable populations. Strengthening resilience is essential not only for mitigating climate-related health impacts but also for ensuring long-term system sustainability amid accelerating environmental change.

AI-Powered Decision Support Systems in Clinical Nursing Practice: Benefits and Ethical Considerations

Ali Khan, Ahmed Shah, Fatima Malik, Rustiyana Rustiyana — Mon, 22 Dec 2025 00:00:00 +0700

Rapid advancements in artificial intelligence (AI) have introduced new possibilities for enhancing clinical nursing practice through AI-powered decision support systems (DSS). Increasing patient complexity, rising workloads, and the demand for timely, evidence-based care underscore the need for technological tools that can assist nurses in clinical judgment, risk assessment, and care coordination. This study aims to examine the benefits and ethical considerations associated with the integration of AI-driven DSS in nursing practice, focusing on their impact on clinical efficiency, patient safety, and professional autonomy. A mixed-methods approach was employed, combining a systematic literature review with qualitative thematic analysis of documented nursing experiences across diverse healthcare settings. The findings reveal that AI-powered DSS improve accuracy in clinical decision-making, support early detection of patient deterioration, and reduce cognitive workload for nurses. The results also identify significant ethical challenges, including data privacy concerns, algorithmic bias, reduced human oversight, and potential shifts in nurse–patient relational dynamics. The study concludes that while AI-driven DSS offer substantial benefits for clinical nursing practice, their implementation must be guided by robust ethical frameworks, transparent governance, and continuous professional training to ensure responsible and equitable integration.

Big Data and Epidemiology: Predictive Models for Future Infectious Disease Outbreaks

Munkhzul Ganbat, Baatar Tserendorj, Selenge Batbold, Rustiyana Rustiyana — Mon, 22 Dec 2025 00:00:00 +0700

Intensifying global mobility, climate variability, and urban density have increased the frequency and complexity of infectious disease outbreaks, prompting the need for more accurate and timely epidemiological surveillance. Big Data analytics has emerged as a transformative approach capable of integrating heterogeneous datasets to detect patterns that traditional surveillance systems often miss. This study aims to examine the effectiveness of predictive modeling techniques leveraging Big Data sources—such as social media activity, electronic health records, mobility data, and environmental indicators—in forecasting potential infectious disease outbreaks. A mixed-methods analytical design was employed, combining machine learning–based predictive modeling with retrospective epidemiological validation using multi-country datasets covering the past ten years. The results show that ensemble learning models, especially random forest and gradient boosting algorithms, significantly outperform conventional statistical models in predicting outbreak onset and trajectory, achieving higher accuracy, sensitivity, and early-warning lead time. The findings demonstrate that Big Data–driven predictive models can enhance public health preparedness by providing earlier and more reliable outbreak alerts. The study concludes that integrating Big Data analytics into national and global epidemiological systems is essential for strengthening proactive disease prevention, although ethical governance and data privacy protections must be prioritized.

Mobile Health (mHealth) Applications: Transforming Preventive Health and Patient Engagement

Aung Myint, Nandar Hlaing, Zaw Min Oo, Rustiyana Rustiyana — Mon, 22 Dec 2025 00:00:00 +0700

The rapid expansion of mobile health (mHealth) applications has reshaped contemporary healthcare by enabling continuous preventive health management and enhancing patient engagement beyond traditional clinical settings. This study aims to examine how mHealth applications contribute to preventive health behaviors and patient engagement, with particular attention to usage patterns, behavioral outcomes, and contextual influences. A mixed-methods research design was employed, combining quantitative analysis of user surveys and application usage data with qualitative insights from interviews and a community-based case study. The findings reveal that frequent and sustained use of mHealth applications is associated with improved preventive behaviors, including increased physical activity, better dietary adherence, and enhanced medication compliance. The results also show that mHealth applications strengthen patient engagement by fostering greater health awareness, self-efficacy, and interaction with healthcare providers. However, variations in outcomes are influenced by digital literacy, personalization features, and the level of contextual support available to users. The study concludes that mHealth applications function as effective tools for transforming preventive health and patient engagement, but their impact depends on user-centered design and integration within broader health systems. The novelty of this research lies in its integrated analysis of preventive health outcomes and patient engagement within a single empirical framework, highlighting mHealth as a foundational component of modern, participatory preventive healthcare.

Interdisciplinary Collaboration in Future Healthcare: Building Holistic Patient-Centered Models

Agnomelsya Bangaran, Adam Idris, Siti Mariam — Mon, 22 Dec 2025 00:00:00 +0700

Healthcare systems are increasingly challenged by complex patient needs, chronic conditions, and fragmented service delivery. Interdisciplinary collaboration has been widely promoted as a strategy to support holistic and patient-centered care, yet its practical implementation in future healthcare systems remains uneven and context-dependent. This study aims to examine how interdisciplinary collaboration contributes to the development of holistic patient-centered models in future healthcare and to identify key factors influencing its effectiveness across different healthcare contexts. A qualitative descriptive approach was employed using a systematic review of secondary data and comparative case analysis. Data were collected from peer-reviewed literature, policy reports, and documented interdisciplinary care models across various regions. The data were analyzed through thematic synthesis and cross-case comparison. The findings indicate that interdisciplinary collaboration enhances care coordination, patient engagement, and holistic service delivery when supported by organizational commitment, shared leadership, and effective communication. Collaboration outcomes vary according to healthcare system maturity, policy alignment, and professional culture. Patient involvement emerges as a critical element in successful interdisciplinary models. The study concludes that interdisciplinary collaboration functions as an adaptive socio-organizational mechanism rather than a uniform solution. The novelty of this research lies in its contextualized analysis, demonstrating that holistic patient-centered care in future healthcare depends on the alignment of collaboration practices with system readiness and patient participation.

Blockchain for Medical Records: Ensuring Security, Privacy, and Interoperability in Global Health

Ali Reza, Leila Mahdavi, Fatemeh Hashemi, Rustiyana Rustiyana — Mon, 22 Dec 2025 00:00:00 +0700

Medical records play a critical role in healthcare delivery, yet existing digital health systems face persistent challenges related to data security, patient privacy, and interoperability across institutions and national borders. Blockchain technology has been proposed as a potential solution to these challenges, particularly within the context of global health systems characterized by fragmentation and regulatory diversity. This study aims to examine how blockchain technology is applied to medical record management in global health, with a focus on its capacity to ensure security, protect privacy, and support interoperability across healthcare systems. The study adopts a qualitative descriptive approach using systematic literature review and comparative case analysis. Secondary data are collected from peer-reviewed publications, policy reports, and documented blockchain-based medical record implementations across multiple regions. Data are analyzed through thematic synthesis and cross-case comparison. The findings reveal that blockchain adoption in medical records is unevenly distributed across regions and is strongly influenced by digital infrastructure readiness and regulatory environments. Security enhancement emerges as the primary implementation outcome, while interoperability gains depend on governance structures and institutional collaboration. Hybrid blockchain architectures combining on-chain metadata with off-chain data storage dominate current practices. The study concludes that blockchain functions as an adaptive socio-technical infrastructure rather than a universal solution for medical records.

Ethical Challenges of AI in Medicine: Balancing Innovation, Privacy, and Equit

Ahmet Demir, Baran Akbulut, Sulastry Pakpahan, Rustiyana Rustiyana — Tue, 30 Dec 2025 00:00:00 +0700

Rapid advancements in artificial intelligence (AI) have transformed medical practice, offering unprecedented capabilities in diagnosis, treatment planning, and predictive analytics. These innovations, however, introduce complex ethical challenges related to patient privacy, algorithmic transparency, equity, and accountability. Growing reliance on AI in clinical environments has heightened concerns about data governance, bias in machine learning models, and uneven access to AI-enabled healthcare tools. This study aims to analyze the ethical tensions arising from AI integration in medicine and to identify strategies that balance technological innovation with the protection of fundamental ethical principles. A qualitative meta-synthesis approach was employed, drawing on peer-reviewed literature, policy documents, and real-world case analyses to examine patterns of ethical risk and mitigation frameworks. The findings reveal that privacy vulnerabilities, inequitable algorithmic performance, and opacity in decision-making processes represent the most frequent ethical concerns. The results also show that robust governance structures, transparent AI design, and inclusive dataset practices significantly reduce ethical risks. The study concludes that responsible AI in medicine requires a multidimensional ethical framework that integrates patient rights, algorithmic fairness, and institutional accountability. Strengthening interdisciplinary collaboration and regulatory oversight is essential for ensuring that AI-driven innovation benefits all patients equitably.