Artificial intelligence (AI) is reshaping healthcare, but without safeguards it can entrench disparities and erode trust. We advance an Equity-by-Design governance approach that embeds fairness audits, ethical oversight, and transparency across the AI lifecycle. Drawing on literature and regulation (e.g., EU AI Act, FDA GMLP) and two case studies, we show how continuous auditing, lifecycle checkpoints, institutional review, and post-deployment monitoring surface and mitigate demographic bias. We outline a practical architecture (metrics, dashboards, documentation) aligned with clinical workflows and regulatory duties, and conclude with policy and research actions to operationalize equity, accountability, and trustworthy deployment in diverse healthcare settings.
Mobile health applications are increasingly used in Indonesia, yet little is known about how users evaluate and experience these tools. This study aims to identify user needs, challenges, and expectations from popular Indonesian mHealth applications by analysing publicly available app reviews. We applied a mixed-methods approach, combining automated preprocessing with qualitative thematic coding. The dataset included 100,000+ user reviews from leading mHealth apps, with analysis supported by inter-rater reliability checks and AI-assisted keyword identification. The findings reveal recurring issues of usability, reliability, and trust, alongside user expectations for affordable services, responsive customer support, and integration with the national healthcare system. Based on these insights, we propose prioritised design and policy recommendations, considering implementation complexity, success metrics, and cultural context. This study contributes to the design of inclusive, trustworthy mHealth platforms in low- and middle-income countries, and informs both academic research and practitioner development.
In the current landscape of economic uncertainty and escalating healthcare costs, health insurance plays a pivotal role as a vital financial tool. The projected surge in healthcare expenditures in Indonesia, as outlined by the Mercer Marsh Benefits Trend Health 2023 report, underscores the urgency for robust health insurance solutions to mitigate the financial risks associated with unpredictable health events. This paper explores the evolving dynamics of health insurance in Indonesia against the backdrop of rising healthcare costs and the growing awareness among the populace regarding the significance of financial protection. We also delve into the burgeoning field of Industry 4.0 and its implications for health insurance claim processing, emphasizing the role of Optical Character Recognition (OCR) technology in automating and streamlining claim administration processes. The study underscores the criticality of optimizing service quality within health insurance companies and delineates the claim decision process, elucidating the pivotal role of technological advancements such as OCR in enhancing efficiency and accuracy. Furthermore, we offer insights into the challenges and considerations associated with OCR implementation and present a comparative analysis of claim decision processes with and without OCR. Overall, the research underscores the importance of leveraging technology and expert human oversight to navigate the complexities of health insurance in Indonesia and ensure swift, equitable, and cost-effective claim resolutions.
Traditional manual review methods are plagued by issues such as being time-consuming, labor-intensive, and prone to inconsistent standards. To overcome these limitations, an intelligent medical record review assistant was developed in conjunction with the cardiac surgery department of a tertiary Grade A hospital, leveraging the advanced text comprehension and reasoning of large language models (LLMs). This assistant, built on the Dify platform, employs the divide-and-conquer strategy that structures the review into a three-stage visual workflow: text structuring, multi-dimensional parallel analysis, and results aggregation. During the parallel analysis stage, prompt engineering techniques, including Expert Mimicry and Chain-of-Thought, direct multiple LLM nodes to independently assess records across four key dimensions: medical terminology, content completeness, diagnostic rationale, and the appropriateness of the treatment plan. Experimental results demonstrate that LLMs outperform both traditional machine learning and deep learning methods in medical record reviewing, and that the divide-and-conquer strategy yields better outcomes than using a single LLM.
Telemedicine has emerged as a transformative approach to delivering healthcare services, particularly during the COVID-19 pandemic, by enabling remote access to medical consultations and monitoring. However, many healthcare workers remain inadequately prepared for this digital shift. This study aimed to evaluate healthcare workers’ readiness for telemedicine implementation across four key dimensions: core readiness, e-learning readiness, clinical readiness, and technology readiness. A cross-sectional descriptive study was conducted using convenience sampling. Data were collected through an online questionnaire adapted from the Core, Clinical, and E-Learning Readiness Assessment and the Technology Readiness Assessment tools. A total of 60 healthcare workers participated voluntarily. Descriptive statistics were used to analyze readiness levels in each domain. Among respondents, 30
Air Quality Index (AQI) has been a viral issue since the Coronavirus Disease 2019 (COVID-19) outbreak worldwide. Much research has been published on the topic of COVID-19 and its correlation with the air quality parameters such as Ozone (O3), Nitrogen Dioxide (NO2), Particulate Matter (PM) with microns size of 2.5, 10, and other parameters related to the measurement of the air quality. This paper research proposed and continued the issue of the correlation between the air quality parameters with the pandemic of COVID-19 and compared it to the improvement of the traffic location in Jakarta. We used several air quality parameters to measure the AQI in Jakarta during COVID-19. The air quality parameters are NO2, SO2, CO, O3, and PM10. The results showed a significant improvement in the air quality parameters when compared pre and during COVID-19. Some locations of research that are Kebon Jeruk and Kelapa Gading, have the better air quality since the pandemic COVID-19 meanwhile, other research locations that are Lubang Buaya, Jagakarsa, and Thamrin, did not have a significant impact on the COVID-19.
Heart disease is one of the diseases known to have highest mortality rate, with estimated that one in third of death per year is caused by heart disease. Heart disease comes randomly and can’t be predicted on whether a patient has a heart attack. Potential of heart attack can be predicted with analysing the health situation of the patient. This research is used to predict the heart disease potential in a patient. This research uses statistical methods, namely Logistic Regression and uses deep learning, namely Neural Network. Comparison between the two models is expected to produce the best model for predicting heart disease. In this experiment the neural network has a very high accuracy reaching from 80
Physician disengagement from telemedicine remains a critical yet underexplored challenge to the long-term viability of digital health systems. While adoption has been widely promoted, the reasons clinicians reduce or discontinue its use after initial uptake are less understood. This study explores the sociotechnological factors influencing Filipino physicians’ decisions to scale back telemedicine use in post-adoption settings. Using a qualitative design, in-depth interviews were conducted with physicians across various specialties and practice locations. Thematic analysis revealed that discontinuance is shaped by a combination of diagnostic limitations, poor system usability, lack of interoperability, medico-legal concerns, emotional exhaustion, and financial burden. Physicians reported that virtual consultations often hindered accurate assessments, strained communication, and disrupted professional boundaries. This research offers a novel contribution by reframing telemedicine disengagement as a professional and clinical recalibration rather than a mere reaction to technical issues. It extends discontinuance models by integrating clinical judgment, ethical accountability, and work-life balance considerations. The study provides practical insights for designing telemedicine systems that are not only technologically reliable but also aligned with physicians’ workflow realities and care standards. These findings are particularly relevant for health systems in low- and middle-income countries seeking sustainable digital health integration.
Recent advances in neuroimaging, genomics, and other technology driven data acquisition methods have greatly increased the complexity and volume of medical data. Traditional machine learning (ML) approaches are becoming increasingly difficult to apply in this context, particularly in neuroscience, where datasets are often high-dimensional but contain a limited number of samples because of the difficulty of collecting data from human participants. Although ML techniques are powerful tools for analyzing large datasets, they typically require substantial training sets containing balanced data and accurate labels. In real-world medical research, such data is rather rare. Consequently, small sample sizes can introduce bias in model performance estimates, thereby limiting the feasibility of predictive modeling. Nevertheless, such datasets are essential for identifying potential biomarkers and for conducting pilot or feasibility studies within the framework of personalized medicine. However, the limited sample size can lead to biased machine learning performance estimates, which makes it impossible to apply ML methods to predictive modeling. Therefore, artificial intelligence-based data mining tools are being developed to process large volumes of data and explore hidden features and correlations. This narrative review provides an overview of ML strategies tailored to neurological datasets with limited sample sizes, to better understand recent trends in this area and identify opportunities for future research. Particular attention is given to dimensionality reduction in complex data with few instances, as well as the integration of data mining and statistical learning techniques to improve the analysis and interpretation of small-scale but information-rich datasets.
Sexually transmitted diseases (STDs) and other sexual disorders continue to pose significant global health challenges, with cases steadily increasing, including in Indonesia. In Bandung, the rising incidence and low public awareness underscore the need for facilities that combine medical rehabilitation with comprehensive sexual education. Current rehabilitation centers often lack psychological support and user oriented spatial design, while stigma and shame hinder individuals from seeking help. This study proposes an interior design concept for a Sexual Rehabilitation and Education Center in Bandung based on the Positive Environment Design approach. The design seeks to reduce stigma and promote emotional healing through human-centered strategies. Its conceptual framework Revival, Encourage, Sanctuary, and Privacy is manifested through zoning, materials, lighting, and circulation. Using a qualitative method involving literature reviews, field observations, interviews, and fishbone diagram analysis, the research identifies user needs that inform the spatial arrangement of medical, educational, and support areas. The resulting prototype demonstrates how interior design can integrate functionality and psychological comfort, serving as a model for future sensitive healthcare environments in Indonesia.
The issue of preserving sensitive information in medical datasets while maintaining their utility is a significant concern in the implementation of Differential Privacy (DP). This paper presents a framework for allocating privacy budgets, designed to optimize the total budget for medical datasets and thereby enhance data utility. Previous strategies for allocating privacy budgets have primarily relied on fixed mathematical rules, and excessive or insufficient noise addition can impact data utility. Therefore, we propose a Genetic Algorithm (GA)-based framework that generates a privacy budget sequence through selection, crossover, and mutation operations to arrive at an attribute-wise optimal privacy budget. After that, the same individual optimal budget is utilized for each record in publishing, ensuring individual privacy guarantees. Experimental findings on two medical datasets reveal enhanced data utility when compared to heuristic budget allocation methods. This framework presents a straightforward and efficacious strategy for allocating privacy budgets within the context of privacy-preserving medical data publication. The source code used in this study is publicly available at https://github.com/Wayne-on-the-road/OPBA-MDP .
The detection of focus and unfocus is critical in education, healthcare, and human–computer interaction. Electroencephalography (EEG) offers a non-invasive and real-time approach to assessing brain activity related to attention, yet challenges persist due to individual variability, non-stationary signals, noise, and limited labeled datasets. This paper reviews current trends in EEG-based focus detection, with an emphasis on deep learning (e.g., CNN, LSTM), meta-learning (MAML), and self-supervised learning (SSL). Publication analysis shows a significant rise in interest, with deep learning studies increasing from 10 in 2015 to more than 3,000 in 2025, while SSL and meta-learning have rapidly emerged since 2020. Our contributions are threefold: (1) identification of major challenges in EEG-based attention detection, including data scarcity and adaptability across subjects; (2) comparative evaluation of learning strategies in terms of data requirements, adaptability, and computational complexity; and (3) discussion of implementation pathways and application areas spanning brain–computer interfaces, neurofeedback, education, mental health, and autonomous systems. This review highlights promising methodologies for improving accuracy, generalizability, and efficiency, underscoring the potential of adaptive AI-driven EEG systems to advance both research and real-world applications.
The detection of somatic mutations and Copy number alterations (CNAs) in cancer cells is crucial for diagnosis and careful observation; however, existing traditional methods are both insufficient and inefficient. Examining the latest advances in Machine learning (ML) techniques, such as the Random forest algorithm, and ensemble models such as SomaticSeq, which turns out to have good mutation detection accuracy and efficiency. This method also proves capable of handling a variety of sample purity and sequencing strategies. Therefore, this method can offer good results and efficiency levels when compared to conventional approaches. Although there are still challenges, such as the need for a capable training dataset and high computational requirements, this ML model promises to make significant progress in cancer diagnosis, early detection, and personalized treatment. This review paper aims to review the ML methods that involve the detection of somatic mutations and (CNA). The results of the review showed that the ML method was promising in both implications, as proven by the minimum accuracy being above 70
Deep learning and large language models (LLMs) are being investigated to detect depression from social media data. However, traditional deep learning methods such as Long Short-Term Memory (LSTM) are limited by their reliance on feature engineering, resulting in time consuming and hard to capture complex patterns arising from media data. To address this issue, we propose a novel deep-learning approach that is based on Bidirectional Encoder Representations from Transformers (BERT) framework with Transformer Regression (called BERT-TR) for depression severity prediction from media data. In addition, we employ different techniques such as under sampling, oversampling and weighted loss function to ensure robust and generalizable predictions for depression. The data from X, derived from clinical assessments aligned with the fifth edition of the (DSM-5-TR) criteria, is used for investigations. We theoretically and empirically show that BERT-TR can predict depression severity from media data effectively and efficiently.
Healthcare branding is increasingly central to reputation, trust, and service quality. This PRISMA-guided systematic review synthesizes 34 peer-reviewed studies (2019–2025) identified from Scopus and related sources. Findings show dominance of quantitative designs using SEM/PLS-SEM, rising digital and AI-driven branding, and hospital-focused contexts, with growing attention to telemedicine and medical tourism, especially in developing countries. Bibliometric mapping (VOSviewer) and thematic analyses consolidate key theories, Brand Equity, Service Quality, Brand Trust, and Smart/Digital Branding. We outline gaps in qualitative evidence, cross-cultural comparisons, and explainable AI, and practical implications for strategies aligned with SDGs 3, 10, and 17.
The government's policy on sustainable development is that there are sustainable development goals that aim to improve the welfare of the world community and preserve nature through 17 main factors with 169 agreements as targets. The third program of sustainable development goals is better health and well-being. This goal talks about better health and well-being for all people, guaranteeing access to basic health services, and protecting all people for well-being. This program needs to be disseminated to the whole community through various means including online by utilizing information technology. This research was conducted to evaluate the knowledge management system which contains a substance on SDG: 3, data analysis was carried out using factor analysis and regression analysis was also used to build mathematical models. The results of this study show that new factors have been formed, namely: socialization culture, socialization innovation, socialization technology, and socialization governance that need to be considered for the success of SDG 1 performance measurement: socialization without poverty through the use of knowledge management systems in the future.
Eating disorders are among the mental health conditions contributing to the highest mortality rates globally. Indonesia ranks 4th in the world in terms of the number of eating disorder patients. Despite the high prevalence of eating disorders in Indonesia, many people still associate their causes solely with milieu and social factors. However, this assumption is incorrect, as several types of eating disorders have been proven to have links to the biological genes present in the human body. Individuals with eating disorders require serious and comprehensive care. The design of rehabilitation centers for eating disorder patients must be capable of responding to their cognitive conditions, including emotional states and behavioral patterns. Unfortunately, current rehabilitation facility designs often fail to fully address these individual needs. This highlights the urgency for a specific design framework that aims to produce environments that are both ideal and sustainable. Gunderson’s 1978 milieu therapy principles, originally developed as behavioral treatment strategies for psychiatric wards, offer a promising foundation for addressing users’ emotional and behavioral needs through healthcare instruments services. By conducting a deeper analysis of these principles and translating them into spatial elements, it is possible to formulate a design guideline that effectively responds to both the emotional and behavioral dimensions of individual users. Ultimately, this approach seeks to create a sustainable healing environment, one that not only supports therapeutic goals but also fosters long-term emotional stability and psychological recovery of the wellbeing.
Electroencephalogram (EEG) signal analysis plays a significant role in recognizing brain function and supporting the diagnosis of Epilepsy. Existing graph approaches are binary or use endpoint-only (slope/correlation) weights that do not capture the interior fluctuations and trend departures, which limit robustness for epilepsy detection. Many rely heavily on preprocessing and lack shift/scale robustness, which compromises generalization and reliability across subjects and recording conditions. The research aims to propose a novel SXTD-Weighted Visibility Graph framework with an information-rich edge weighting scheme to enhance interpretability and diagnostic accuracy in epilepsy EEG analysis. In addition, new EEG graph features such as Transitive Amplification Index (TAI), MedianWeightEps are developed that capturing the interior fluctuations and trend deviations, remaining shift-invariant and scale-equivariant, providing tunable noise–structure control, and requiring no additional assumptions. The proposed framework achieved 100
Oxidative stress, resulted from the excessive generation of reactive oxygen species (ROS), contributes in skin aging by damaging cellular macromolecules. Oral supplementation with antioxidant-rich formulations has emerged as a promising approach to counteract skin aging by reducing oxidative stress. TriGlow+ is a health beverage composed of collagen tripeptide, multiple fruit extracts (lemon, apple, carrot, grape seed, green tea, aloe vera, Garcinia atroviridis), and glutathione, which collectively possess antioxidant and anti-aging potential. This study aimed to evaluate the anti-aging and antioxidant activity of TriGlow+. Antioxidant capacity was assessed by DPPH and FRAP assays, while anti-aging activity was measured through elastase and collagenase inhibition assay. DPPH assay revealed dose-dependent free radical scavenging activity with an IC₅₀ of 3014.68 ± 146.82 µg/mL. In contrast, FRAP results demonstrated strong ferric ion reducing power, with values elevated at 50–100 µg/mL (257.55 ± 2.85 and 282.58 ± 2.44 µM Fe (II)/µg). TriGlow+ also inhibited key aging-related enzymes in a concentration-dependent manner. Elastase inhibition IC₅₀ is 46.19 ± 0.31 µg/mL, indicating strong inhibitory potential. Collagenase inhibition exhibited IC₅₀ value of 182.10 ± 1.96 µg/mL, showing moderate efficacy. In conclusion, TriGlow+ exhibits notable antioxidant and anti-aging activities, particularly through ferric reducing power and inhibition of elastase and collagenase. Overall, the study provides foundational evidence supporting the functional benefits of TriGlow+. These findings highlight TriGlow+ as a promising oral nutraceutical for promoting skin health and combating oxidative stress, warranting further investigation into its active components and clinical efficacy.
The rapid expansion of medical tourism has been accompanied by an increasing reliance on telemedicine for pre-operative consultations and post-treatment follow-up care across jurisdictions. While this integration presents promising continuity of care and improvement in patient convenience, it also exposes significant legal gaps in legal liability, regulatory compliance, and data governance. This paper examines the intersection of medical tourism and telemedicine, emphasizing how the absence of harmonized legal frameworks between host and home countries creates challenges for both healthcare providers and patients. Utilizing a normative juridical method combined with a comprehensive legal analysis of prominent medical tourism destinations, the study extracts insights from a thorough literature review to analyse regulatory frameworks in key medical tourism hubs and patient-exporting countries, aiming to identify areas of misalignment and legal ambiguity. It advocates for the establishment of international legal instruments and bilateral agreements that delineate provider responsibilities, ensure informed consent, and protect patient rights in transnational care arrangements. By proposing a model for cross-border legal alignment in telemedicine-based follow-up care, this paper aims to ensure legal certainty, safeguard patient welfare, and create a safer, more coherent system for global healthcare delivery in the era of digital medicine.
