Interpretable Deep Learning Models for Transparent Health Decision Support Systems
The increasing adoption of deep learning in healthcare has created significant opportunities for improving medical diagnosis, patient risk prediction, and clinical decision support. However, many deep learning models remain difficult to interpret, limiting their acceptance in health systems where transparency, accountability, and patient safety are essential. This study aims to develop and evaluate interpretable deep learning models for transparent health decision support systems. The proposed approach integrates convolutional neural networks, deep neural networks, attention mechanisms, SHAP values, and Grad-CAM to explain model predictions across medical imaging and clinical tabular datasets. Public healthcare datasets, including chest X-ray images, electronic health records, and diabetes patient records, are used to represent real clinical decision-making contexts. Model performance is evaluated using accuracy, precision, recall, F1-score, AUC, sensitivity, and specificity, while interpretability is assessed through explanation clarity, feature relevance, decision traceability, and clinical usefulness. The findings indicate that interpretable deep learning models can provide reliable predictive performance while offering clearer explanations for medical decisions. Visual heatmaps and feature attribution outputs help identify relevant disease indicators, patient risk factors, and clinical variables that influence model predictions. This study contributes to the development of trustworthy AI in healthcare by supporting transparent, ethical, and human-centered clinical decision-making. The research is also aligned with SDG 3 by promoting better health outcomes, patient safety, and responsible digital health innovation.
Copyright (c) 2026 Deni Sunaryo, Antonius Ary Setyawan, Po Abas Sunarya, Sondang Visiana Sihotang, Putri Anahera Oganda https://creativecommons.org/licenses/by/4.0
Understanding Trust in AI Symptom Checker Applications Through Empathy and Privacy
The increasing use of artificial intelligence in healthcare has introduced AI symptom checker applications as accessible tools for identifying health conditions and supporting early medical decision making. Despite their growing adoption among young adults, trust remains a critical factor influencing user acceptance of these applications. This study examines the influence of perceived empathy and privacy concerns on trust in AI symptom checker applications among young adults. A quantitative approach was employed using a questionnaire distributed to young adults aged 18 to 35 who had experience using AI-based symptom checker applications or digital healthcare platforms with symptom analysis features. A total of 168 valid responses were collected and analyzed using multiple linear regression through SPSS. The findings indicate that perceived empathy has a positive and significant effect on trust, while privacy concerns have a negative and significant effect on trust. The regression results also show that perceived empathy has a stronger influence than privacy concerns in shaping user trust toward AI symptom checker applications. These findings highlight the importance of integrating empathetic communication features and strengthening data privacy protection in AI healthcare technologies. This study contributes to the development of human-centered healthcare innovation by emphasizing that trust in AI systems is shaped not only by technical functionality but also by emotional interaction and ethical data management.
Copyright (c) 2026 Sunda Ariana, Linda Unsriana, Dedeh Supriyanti, Shakeel Rahagi, Nur Silawati https://creativecommons.org/licenses/by/4.0
AI in Industry Forecasting: The Use of AI in Predicting Industry Trends and Demands
The rapid advancement of Artificial Intelligence (AI) has increasingly transformed industrial operations, yet its application in accurately forecasting industry trends and market demands remains underexplored. This study aims to evaluate the effectiveness of AI-based predictive models in anticipating market shifts and consumer needs across multiple sectors. Employing a quantitative research design, data were collected from 150 industry reports and historical market datasets. Predictive modelling techniques, including machine learning algorithms such as random forests and neural networks, were applied to analyze trends and forecast demand patterns. The results indicate that AI models significantly enhance the accuracy of industry forecasting, reducing prediction errors by up to 25% compared to traditional approaches. These findings suggest that integrating AI into strategic decision-making can optimize resource allocation, improve market responsiveness, and support sustainable industrial development. By enabling more efficient production planning and market adaptation, AI-driven forecasting contributes indirectly to long-term economic and environmental sustainability. Overall, the research demonstrates that AI not only advances technological capabilities but also aligns with broader sustainability objectives.
Copyright (c) 2025 Mohd Faiz Hilmi, Hamdan Hamdan, Adam Faturahman, Bintang Nandana Henry, Jessica Wilson https://creativecommons.org/licenses/by/4.0
AI Assisted Health Monitoring and Digital Inclusion for Emotional Security Among Older Adults
The growing population of older adults has increased the need for accessible and sustainable healthcare support, particularly through digital technologies that enable continuous health monitoring. Recent developments in artificial intelligence have expanded the use of AI assisted health monitoring systems, including wearable devices and remote health applications, to support daily healthcare management among older adults. Beyond clinical functions, these technologies may also contribute to emotional well-being by strengthening feelings of safety and supporting digital inclusion. However, limited qualitative research has examined how older adults experience AI assisted health monitoring in relation to emotional security and access to digital healthcare. This study investigates the role of AI assisted health monitoring in supporting digital inclusion and emotional security among older adults. A qualitative descriptive approach was employed using semi structured interviews with older adult participants who had experience using digital health monitoring technologies. The collected data were analyzed through thematic analysis to identify recurring experiences and perspectives. The findings indicate that AI assisted health monitoring contributes to a stronger sense of emotional security by helping older adults monitor health conditions more consistently and reducing uncertainty regarding daily health management. Family assistance also plays an important role in supporting digital inclusion, while technology related barriers remain a challenge for some participants. These findings highlight the importance of designing human centered AI healthcare systems that are accessible and emotionally supportive for aging populations. The study contributes to discussions on healthy aging, digital inclusion, and empathetic healthcare innovation within AI based healthcare environments.
Copyright (c) 2026 Dian Indiyati, Mulyati, Christopher Davis, Sheila Aulia Anjani, Mardaleni Daeli https://creativecommons.org/licenses/by/4.0
Empathetic AI for Preventive Healthcare in Smart Air Quality Monitoring
Air pollution has become one of the most critical global public health challenges, contributing to respiratory diseases, cardiovascular conditions, mental stress, and reduced quality of life, particularly in urban regions. Conventional air quality monitoring systems primarily focus on technical measurements and pollutant indicators while often neglecting emotional, behavioral, and social dimensions of environmental healthcare communication. This study proposes a human-centered framework integrating Empathetic Artificial Intelligence (EAI) into Smart Air Quality Monitoring Systems (Smart AQMS) to enhance preventive healthcare and sustainable community wellbeing. The novelty lies in combining IoT-based environmental sensing, AI-driven predictive analytics, and empathy-oriented communication mechanisms within a unified environmental healthcare ecosystem aligned with Orange Technology principles. Using a qualitative conceptual methodology through interdisciplinary literature synthesis, the study explores relationships among environmental health monitoring, empathetic computing, healthcare informatics, and human–computer interaction. The proposed framework includes real-time pollutant detection, cloud-based decision support systems, personalized healthcare recommendations, and emotionally intelligent communication interfaces to improve health awareness and preventive behavioral responses. The study proposes that an empathetic Smart AQMS may strengthen public trust in digital healthcare systems, enhance environmental health literacy, reduce psychological distress during pollution events, and support vulnerable populations through culturally sensitive communication. Furthermore, the framework contributes to Sustainable Development Goals (SDGs 3, 9, 11, and 13) by promoting inclusive and sustainable healthcare innovation. This study provides theoretical contributions to empathetic AI research and offers implications for policymakers, healthcare institutions, and smart city developers in designing compassionate environmental healthcare systems.
Copyright (c) 2026 Syahyono, Dwi Apriliasari, Nesti Anggraini Santoso, Reymund Rahardja, Oliver Sauntos https://creativecommons.org/licenses/by/4.0
Blockchain Technology Implementation and Innovation of Blockchain Technology Across Various Industries
The rapid development of Blockchain technology has paved the way for its integration and innovation across various industries, ranging from finance to healthcare, supply chain management, and beyond. Blockchain technology, initially designed for cryptocurrencies, offers enhanced security, transparency, and decentralized structures, making it an attractive solution for numerous sectors. This paper aims to explore the implementation of Blockchain technology and its impact on different industries, focusing on its practical applications and innovative uses. A qualitative research approach is adopted, utilizing case studies and industry reports to analyze how blockchain is being incorporated into various sectors. The findings reveal that industries such as finance, healthcare, and logistics have leveraged Blockchain for improved transparency, reduced fraud, and enhanced operational efficiency. Notable innovations include decentralized finance (DeFi), digital healthcare records, and transparent supply chains. Blockchain technology is revolutionizing industries by offering new solutions to long-standing challenges. The continuous innovation in Blockchain will likely reshape traditional business models and foster further advancements. The study highlights the transformative potential of Blockchain and suggests further research into its scalability and integration with emerging technologies.
Copyright (c) 2025 Abdul Wahab Abdul Rahman, Noor Azura Zakaria, Fitra Putri Oganda, Greisy Jacqueline https://creativecommons.org/licenses/by/4.0
The Influence of Artificial Intelligence Learning Systems on Student Learning Effectiveness
The rapid development of artificial intelligence has significantly influenced learning systems in higher education. Artificial intelligence learning systems provide various features that support the learning process, including personalized learning assistance, instant feedback, and easier access to information. However, the effectiveness of artificial intelligence in improving student learning outcomes still requires empirical investigation. This study aims to examine the influence of artificial intelligence learning systems on student learning effectiveness. Specifically, the study analyzes the effects of AI learning usability, AI learning support, and learning motivation on student learning effectiveness. This research employed a quantitative approach using a survey method. Data were collected from university students who utilize artificial intelligence tools in their learning activities. A structured questionnaire using a Likert scale was distributed to respondents to measure the research variables. The collected data were analyzed using multiple linear regression analysis to examine the relationships between variables. The results of the analysis indicate that AI learning usability, AI learning support, and learning motivation have significant positive effects on student learning effectiveness. These findings suggest that the effective implementation of artificial intelligence in learning environments can enhance students’ understanding, engagement, and overall learning outcomes. The study highlights the importance of integrating artificial intelligence learning systems as an innovative approach to improve educational effectiveness in higher education.
Copyright (c) 2025 Tri Pujiati, Ipang Sasono, Shih-Chih Chen, Nuke Puji Lestari Santoso, Nova Syahrani Arasid https://creativecommons.org/licenses/by/4.0
Development and Integration of IoT for Smart Systems and Sustainable Infrastructure
The rapid growth of digital technology has accelerated the adoption of the Internet of Things (IoT) in the development of smart systems across multiple sectors. However, challenges related to system integration, scalability, and efficient data management remain significant barriers to optimal implementation. This study aims to design IoT-based intelligent systems that enable real-time monitoring, automation, and intelligent decision-making for various applications. The research employs a system development approach combining IoT devices, sensor networks, cloud platforms, and data analytics to create an interconnected smart environment. The proposed framework is implemented and evaluated through several application scenarios, including smart environment monitoring and smart infrastructure management. The results indicate that coordinated IoT deployment improves operational efficiency, enhances data accuracy, and supports adaptive system responses through continuous data processing. Furthermore, the developed system contributes to sustainable technological advancement aligned with the Sustainable Development Goals (SDGs) by promoting efficient resource utilization and smarter infrastructure management. In conclusion, the proposed architecture provides a scalable and flexible foundation supporting innovation, sustainability, and digital transformation across diverse application domains.
Copyright (c) 2026 Andriyansah, Ruli Supriati, Harry Agustian, Rani Nuraeni, Murphy Mutie https://creativecommons.org/licenses/by/4.0
Smart Hydration Analytics Using Artificial Intelligence for Personalized Preventive Healthcare
Dehydration remains a significant yet frequently overlooked public health concern, particularly in tropical environments where high temperatures, humidity, and intensive daily activities increase the risk of physiological imbalance among students, workers, athletes, and urban populations. Conventional hydration management approaches primarily depend on self-awareness and manual water intake tracking, which often fail to provide accurate and real-time assessment of individual hydration needs. This study aims to propose a Smart Hydration Analytics framework utilizing Artificial Intelligence (AI), Internet of Things (IoT), wearable sensing technologies, and personalized healthcare analytics to support preventive healthcare and sustainable digital wellbeing. Using a conceptual research methodology supported by a systematic literature review, this study synthesizes interdisciplinary insights from AI-driven healthcare systems, wearable monitoring technologies, preventive medicine, and human-centered healthcare innovation. The proposed framework consists of five integrated layers, namely Physiological Sensing Layer, Data Intelligence Layer, Predictive Hydration Analytics Layer, Personalized Recommendation Layer, and Ethical Healthcare Governance Layer. The novelty of this research lies in the integration of predictive AI modeling, adaptive hydration recommendations, and human-centered healthcare interaction within a sustainable preventive healthcare ecosystem specifically designed for tropical environments. The findings indicate that AI-powered hydration analytics systems can improve early dehydration risk detection, optimize personalized fluid intake recommendations, enhance healthy lifestyle awareness, and strengthen real-time preventive healthcare management. Furthermore, the framework supports the principles of human-centered AI and Orange Technology by emphasizing wellbeing, accessibility, ethical healthcare governance, and sustainable digital healthcare interaction. This study contributes theoretically to the development of intelligent preventive healthcare systems and provides a conceptual foundation for future empirical implementation of smart hydration monitoring technologies.
Copyright (c) 2026 Desy Apriani, Retwando Retwando, Shofiyul Millah, Eka Devonte Archer https://creativecommons.org/licenses/by/4.0
Advancing Preventive Community Medicine for Enhancing Public Health Awareness
Preventive community medicine is essential for promoting public health and reducing the burden of preventable diseases. With the rise of emerging technologies, especially Artificial Intelligence (AI), there is an opportunity to enhance the effectiveness of preventive health initiatives. This paper explores the integration of AI in preventive community medicine to enhance public health awareness and improve preventive healthcare strategies. The research utilizes a qualitative approach, analyzing case studies, interviews with healthcare professionals, and literature on AI applications in public health. The study investigates how AI technologies can be incorporated into community-based health interventions. The findings indicate that AI, particularly in data analytics and personalized health education, has a significant impact on improving public health awareness. AI tools such as mobile apps and predictive models enable real-time health interventions and offer tailored health advice. The integration of AI in preventive community medicine holds great potential to improve public health outcomes. The use of AI-driven technologies for early disease detection and health education can revolutionize public health campaigns, making them more effective and accessible.
Copyright (c) 2025 Athapol Ruangkanjanases, Nikita Ivanov, Muhtarom Muhtarom, Markus Salmi, Ninda Lutfiani https://creativecommons.org/licenses/by/4.0
An Exhaustive Survey to Understand Music Generation based on Artificial Intelligence
Music generation is a creative process that requires the ability to understand melody, rhythm, harmony, structure, and emotional expression. Although music has long been viewed as a human-centered artistic domain, the development of Artificial Intelligence has opened new opportunities for automatic music generation with minimal human intervention. This study aims to analyze the use of AI algorithms in music generation, identify commonly used approaches, and examine existing gaps in producing high-quality musical compositions. The study reviews several AI-based methods, including recurrent neural networks, Deep Composer, WaveNet, memetic algorithms, generative adversarial networks, reinforcement learning, and transformer-based models. In addition, publicly available music datasets and GAN-based synthetic data generation are considered to support the training process. The findings indicate that deep learning models are effective in learning musical patterns, while reinforcement learning and transformer-based preprocessing can improve sequence understanding, adaptability, and structural coherence. However, current models still face challenges in duplicating specific artist styles, maintaining complete song structure, and generating music with strong uniqueness. Therefore, integrating deep learning, reinforcement learning, GAN-based synthetic data, and transformer preprocessing offers a promising direction for improving AI-generated music quality and supporting future research in automatic music composition.
Copyright (c) 2026 Bhupesh Rawat, Qurotul Aini, Nuke Puji Lestari Santoso, Sipho Dlamini https://creativecommons.org/licenses/by/4.0
Exploring Public Experiences Using Telemedicine Services In Digital Healthcare Delivery Systems
The rapid development of digital health technologies has accelerated the adoption of telemedicine services in healthcare delivery systems. Telemedicine enables publics to access medical consultations remotely, offering potential improvements in healthcare accessibility, efficiency, and continuity of care. However, understanding public experiences remains essential to evaluate the effectiveness and acceptance of telemedicine in real healthcare settings. This study aims to explore public experiences in using telemedicine services within digital healthcare delivery systems. A qualitative research design was employed using in depth interviews with seven informants who had prior experience utilizing telemedicine platforms for healthcare consultations. Participants were selected using purposive sampling to ensure relevant experience with digital health services. Data were collected through semi structured interviews and analyzed using thematic analysis to identify recurring patterns and key themes related to telemedicine usage. The findings reveal several important themes including accessibility of healthcare services, perceived efficiency in consultation processes, communication dynamics between publics and healthcare providers, and technological challenges encountered during telemedicine interactions. While telemedicine improves convenience and reduces travel time for publics, concerns regarding internet connectivity and limitations in doctor public interaction remain significant issues. The study highlights the importance of improving digital infrastructure and enhancing communication strategies in telemedicine services. These findings provide insights for healthcare providers and policymakers in optimizing telemedicine implementation in digital healthcare systems.
Copyright (c) 2025 Kustiyono, Etty Puji Lestari, Nur Azizah, Anandha Fitriani, Brigitta Callula https://creativecommons.org/licenses/by/4.0
Heart Attacks and Brain Strokes in Early, Mid and Late Age: Study From Data Science Perspective
Heart attacks and brain strokes have become major global health concerns due to the increasing number of cases occurring among not only older adults but also middle-aged and younger populations. Unhealthy lifestyles, smoking habits, obesity, hypertension, and diabetes have significantly contributed to the rising risk of cardiovascular and neurological diseases. This study aimed to investigate heart attack and brain stroke risks across different age groups from a data science perspective while evaluating the effectiveness of machine learning techniques for disease prediction. The research employed quantitative experimental analysis using healthcare datasets containing clinical and lifestyle-related variables such as age, blood pressure, cholesterol level, glucose level, smoking behavior, body mass index, and diabetes condition. Two supervised machine learning algorithms, namely Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), were implemented to classify disease risks. The experimental results showed that the SVM model achieved superior predictive performance with an accuracy of 99.5%, while XGBoost achieved 94.3% accuracy. The novelty of this study lies in integrating multi-age cardiovascular and neurological risk analysis with artificial intelligence-based predictive modeling to support the development of an intelligent mobile healthcare alert system. The study concluded that machine learning technologies have strong potential to improve early disease prediction, preventive healthcare, and proactive patient monitoring in modern healthcare environments.
Copyright (c) 2026 Ankur Singh Bist, Untung Rahardja, Ninda Lutfiani, Rifqa Nabila Muti, Santiago Ramirez https://creativecommons.org/licenses/by/4.0
Ethical Governance of Health Data in the Age of Artificial Intelligence
The integration of artificial intelligence (AI) in healthcare has revolutionized the industry, offering new opportunities for personalized treatment, early disease detection, and efficiency. However, the ethical management of health data remains a significant challenge. This paper explores the governance of health data in the context of AI, emphasizing the balance between ethical considerations, privacy, security, and technological innovation. This research investigates the ethical issues related to health data governance, including data privacy, security protocols, consent, and the ethical implications of AI-driven healthcare decisions. A qualitative analysis is conducted through a review of existing literature, case studies, and the examination of current regulations and policies surrounding health data governance in healthcare organizations worldwide. The findings highlight critical challenges, including data breaches, patient consent, and the transparency of AI decision-making processes. Key ethical frameworks are proposed to ensure that healthcare organizations and AI developers adhere to robust data governance policies. The study suggests the need for stronger, more transparent regulatory frameworks to protect patient data while fostering innovation. Ethical guidelines are essential to maintaining trust in AI-driven healthcare solutions, ensuring they benefit patients without compromising their privacy.
Copyright (c) 2025 Kristoko Dwi Hartomo, Danny Manongga, Ageng Setiani Rafika, Dwi Julianingsih, Zeze Nanle https://creativecommons.org/licenses/by/4.0
The Role of Perceived Emotional Support in AI Mental Health Chatbots among Generation Z
Artificial Intelligence (AI) mental health chatbots are increasingly used by Generation Z as a source of emotional support and digital companionship. The growing interaction between users and AI systems has raised questions regarding the ability of AI chatbots to provide empathetic communication and contribute to users’ emotional well-being. This study aims to examine the role of perceived emotional support and perceived empathy in AI mental health chatbots on AI companionship and emotional well-being among Generation Z users. This study employed a quantitative approach using survey data collected from 160 Generation Z respondents who had experience using AI mental health chatbots. Data were analyzed using Structural Equation Modeling Partial Least Squares (SEM-PLS) to evaluate the relationships among variables. The findings indicate that perceived emotional support and perceived empathy positively influence AI companionship. Furthermore, AI companionship significantly contributes to emotional well-being. The results also show that perceived emotional support and perceived empathy have direct positive effects on emotional well-being among Generation Z users. AI mental health chatbots have the potential to provide emotionally supportive and empathetic interactions that enhance users’ emotional well-being. The study highlights the importance of integrating human-centered and empathetic features into AI systems to support psychological well-being in digital healthcare environments.
Copyright (c) 2026 Mardiana, Ossi Ferli, Henderi, Evelyn Anderson, Alni Cahyani https://creativecommons.org/licenses/by/4.0
Social Network Analysis in P2P Lending Risk Assessment
This study investigates how social network analysis (SNA) can enhance risk assessment in Peer to Peer (P2P) lending platforms, where the rapid growth of digital lending highlights the background challenge of accurately evaluating borrower credibility beyond traditional financial metrics. The objective of this research is to develop a network based analytical framework that identifies relational patterns among borrowers and lenders to improve the detection of potential default risks. The method integrates graph based modeling, centrality measurements, and community detection algorithms applied to borrower interaction data, enabling the identification of structural features such as influence, connectivity, and hidden borrower clusters that may correlate with credit behavior. The results demonstrate that borrowers with high betweenness and eigenvector centrality values exhibit significantly different default tendencies compared to those in more isolated network positions, while community structures reveal risk concentrated clusters that are not captured by conventional credit scoring systems. The conclusion emphasizes that incorporating SNA into P2P lending risk assessment provides a more holistic and data driven understanding of borrower behavior, allowing platforms to strengthen credit evaluation, reduce default rates, and support more sustainable digital lending ecosystems.
Copyright (c) 2026 Afif Aditya Darmawan, Marviola Hardini, Efa Ayu Nabila, Hana Maria https://creativecommons.org/licenses/by/4.0
Artificial Intelligence Empathy and Patient Satisfaction in Digital Healthcare Services
Artificial intelligence has rapidly transformed healthcare systems by improving efficiency, diagnostic accuracy, and accessibility of medical services. Digital healthcare platforms increasingly integrate artificial intelligence technologies to support telemedicine, health monitoring, and medical consultations. These innovations enable patients to access healthcare services more conveniently and help healthcare providers deliver faster and more efficient care. Namun, the increasing reliance on artificial intelligence in healthcare also raises concerns regarding the lack of emotional interaction and empathetic communication between patients and digital systems. The absence of empathetic responses in AI-based healthcare services may influence patient trust and satisfaction when interacting with digital health technologies. Therefore, understanding the role of machine empathy in artificial intelligence systems becomes important in developing human-centered healthcare services. This study aims to examine the influence of artificial intelligence service quality and machine empathy on patient trust and patient satisfaction in digital healthcare platforms. The research employed a quantitative approach using a survey method involving 150 respondents who had experience using telemedicine applications. Data were collected using an online questionnaire with a five-point Likert scale and analyzed using Structural Equation Modeling. The findings indicate that artificial intelligence service quality and machine empathy significantly influence patient trust and patient satisfaction. Furthermore, patient trust demonstrates a strong positive relationship with patient satisfaction. These results highlight the importance of integrating empathetic capabilities into artificial intelligence systems to improve patient experience in digital healthcare environments.
Copyright (c) 2025 Muhammad Zaki, Umi Rusilowati, Dwi Apriliasari, Jonathan Parker https://creativecommons.org/licenses/by/4.0
Integrating Clinical Intelligence and Emotional Responsiveness to Build Patient Trust in Healthcare Delivery
Artificial Intelligence has significantly transformed diagnostic processes in healthcare by improving speed, pattern recognition, and decision support across clinical settings. However, diagnostic excellence is not determined by accuracy alone. In real-world practice, patients also value compassion, reassurance, trust, and the feeling of being understood during the diagnostic journey. This paper explores the concept of Empathetic AI in Diagnostics, referring to AI systems that not only assist clinical reasoning but also communicate findings in ways that are emotionally responsive, understandable, and supportive of patient needs. Using a literature-based conceptual approach, this study synthesizes recent evidence on diagnostic AI, conversational medical models, artificial empathy, explainable AI, and patient trust. The findings suggest that empathetic AI can strengthen diagnostic interactions by improving patient comprehension, reducing anxiety, and supporting clinicians in delivering more patient-centered care. At the same time, important risks remain, including inauthentic empathy, overreliance on automated advice, bias, privacy concerns, and legal ambiguity. This paper proposes a framework in which empathetic diagnostic AI should be designed around five dimensions: diagnostic accuracy, emotional sensitivity, explainability, clinician oversight, and ethical accountability. The study concludes that the future of diagnostic AI should not focus solely on prediction performance, but also on how AI supports humane, trustworthy, and ethically grounded diagnostic communication.
Copyright (c) 2025 Dyah Juliastuti, Yohana F. Cahya Palupi Meilani, Ihsan Nuril Hikam, Julia Nathalie https://creativecommons.org/licenses/by/4.0
A Novel Deep Learning Technique for Medical Image Analysis Using Improved Optimizer
Application of Convolutional neural network in spectrum of Medical image analysis are providing benchmark outputs which converges the interest of many researchers to explore it in depth. Availability of clean dataset is one of the significant factor which drives the efficiency of CNN (Convolutional neural network) model. Latest preprocessing technique Real Esrgan and GFPGAN (Generative facial prior GAN) are proving their mettle in providing high resolution dataset. Optimizer also plays a vital role in upgrading the functioning of CNN model. A new optimization technique Gradient Centralization is providing the unparalleled result in terms of generalization and execution time. Our research delves deep into aforementioned factors. Result has already been achieved by taking in account the first factor which is preprocessing technique. In continuation of our effort, our paper explores the next factor which is the employment of new optimization technique, Gradient centralization to our integrated framework (Model with advanced preprocessing technique). Preprocessing integrated Densenet 201, and NasNet are giving encouraging result in terms of training loss and execution time when GC is used as optimizer. Experiments are carried out for three different types of datasets: Skin, Retina and Lung and comparison of loss curves for both preprocessing integrated models is carried out.
Copyright (c) 2025 Vertika Agarwal, M.C Lohani, Qurotul Aini, Nesti Anggraini Santoso https://creativecommons.org/licenses/by/4.0
Driver Drowsiness Detection using Novel Deep Learning
Road accidents remain one of the leading causes of death worldwide, with driver drowsiness identified as a major contributing factor, particularly during long-distance travel. Fatigue reduces a driver’s alertness, reaction time, and decision-making ability, significantly increasing the risk of accidents. Therefore, developing an intelligent system capable of detecting driver drowsiness in real time is essential to enhance road safety. Artificial Intelligence (AI), particularly deep learning, has emerged as a promising technological approach to address this issue due to its strong capability in image processing and facial recognition tasks. This study focuses on the development of a deep learning–based driver drowsiness detection system using facial features as the primary indicators of fatigue. The proposed approach involves collecting and preparing a dataset consisting of driver facial images under various conditions, including normal and drowsy states. Key facial indicators such as eye closure, blinking frequency, and yawning patterns are analyzed to identify signs of drowsiness. A convolutional neural network (CNN) architecture is then employed to learn these visual patterns and classify the driver’s state into alert or drowsy categories. The system is designed to operate in real time using a camera-based monitoring mechanism that continuously captures facial data while the driver is operating the vehicle. When signs of drowsiness are detected, the system can trigger an alert to warn the driver and prevent potential accidents. The results demonstrate that deep learning models are highly effective in recognizing fatigue-related facial patterns, providing a reliable solution for intelligent driver monitoring systems. This research contributes to the advancement of AI-based safety technologies aimed at reducing traffic accidents and improving transportation safety.
Copyright (c) 2025 Bhupesh Rawat, Untung Rahardja, Marviola Hardini, Aulia Rahma Dina https://creativecommons.org/licenses/by/4.0