Dr. Jack Ng Kok Wah is a Senior Lecturer at Multimedia University, Cyberjaya, with 30 years of industry experience, an HRDF-accredited trainer, focusing on artificial intelligence in marketing, healthcare, and digital innovation, and using his research to create practical solutions that benefit people and businesses.
ACADEMIC DISTINCTION & RESEARCH EXCELLENCE
1) Published 10+ high-impact Q1 journal papers in AI, indexed in PubMed, Scopus, and WoS.
2) Won multiple international research awards in public health, robotic surgery, and business innovation.
3) Demonstrated strong research influence with 100+ citations and a rising h-index of 8.
4) Gained international visibility with research highlighted by AHCJ and other global platforms.
5) Delivered a major ASEAN keynote on AI and Mental Health to over 300 industry and academic leaders.
6) Strengthened MMU’s global presence by leading research collaboration with Xi’an Jiaotong–Liverpool University.
7) Featured in Britishpedia’s ‘Successful People in Malaysia & Singapore.’
8) Published widely read thought-leadership articles i.e. The Edge Malaysia on AI.
9) Actively contributes as a reviewer and editorial board member for reputable Q1/Q2 international journals.
10) Maintains excellent teaching ratings (average 4.6/5) across MBA, EMBA, and undergraduate programs.
Introduction The integration of Artificial Intelligence (AI) in healthcare, particularly through hybrid chatbots, is reshaping the industry by enhancing service delivery, patient engagement, and clinical outcomes. These chatbots combine AI with human input to provide intelligent, personalized interactions in areas like diagnostics, chronic disease management, and mental health support. However, gaps remain in trust, data security, system integration, and user experience, which hinder widespread adoption. Key challenges include the hesitancy of patients to trust AI due to concerns over data privacy and the accuracy of medical advice, as well as difficulties in integrating chatbots into existing healthcare infrastructures. The review aims to assess the effectiveness of hybrid AI chatbots in improving healthcare outcomes, reducing costs, and enhancing patient engagement, while identifying barriers to adoption such as cultural adaptability and trust issues. The novelty of the review lies in its comprehensive exploration of both technological advancements and the socio-emotional factors influencing chatbot acceptance. Methods The review follows a systematic methodology with four core components: eligibility criteria, review selection, data extraction, and data synthesis. Studies focused on AI applications and hybrid chatbots in healthcare, particularly in chronic disease management and mental health support, were included. Publications from 2022 to 2025 were prioritized, and peer-reviewed sources in English were considered. After screening 116 studies, 29 met the criteria for inclusion. Data was extracted using a structured template, capturing study objectives, methodologies, findings, and challenges. Thematic analysis was applied to identify four themes: AI applications, technical advancements, user adoption, and challenges/ethical concerns. Statistical and content analysis methods were employed to synthesize the data comprehensively, ensuring robustness in the findings. Results Hybrid chatbots in healthcare have shown significant benefits, such as reducing hospital readmissions by up to 25%, improving patient engagement by 30%, and cutting consultation wait times by 15%. They are widely used for chronic disease management, mental health support, and patient education, demonstrating their efficiency in both developed and developing countries. Discussion The review concludes that overcoming these barriers through infrastructure investment, training, and enhanced transparency is crucial for maximizing the potential of AI in healthcare. Future researchers should focus on long-term outcomes, addressing ethical considerations, and expanding cross-cultural adaptability. Limitations of the review include the narrow scope of some case studies and the absence of long-term data on AI’s efficacy in diverse healthcare contexts. Further studies are needed to explore these challenges and the long-term impact of AI-driven healthcare solutions.
Abstract The integration of robotics and artificial intelligence (AI) in surgery represents a transformative advancement in modern healthcare, promising enhanced precision, efficiency, and patient outcomes. Recent studies indicate a rapid adoption of AI-assisted robotic surgery across various surgical specialties, driven by improvements in accuracy and reduced complication rates. The research synthesizes findings from 25 recent peer-reviewed studies (2024–2025) on AI-driven robotic surgery. Systematic review and meta-analyses were conducted focusing on clinical efficacy, surgical precision, complication rates, and economic impacts. Quantitative data were extracted from retrospective trials, cohort studies, and systematic reviews to evaluate outcomes compared to manual surgical techniques. AI-assisted robotic surgeries demonstrated a 25% reduction in operative time and a 30% decrease in intraoperative complications compared to manual methods. Surgical precision improved by 40%, reflected in enhanced targeting accuracy during tumor resections and implant placements. Patient recovery times were shortened by an average of 15%, with lower postoperative pain scores. Additionally, studies reported an average 20% increase in surgeon workflow efficiency and a 10% reduction in healthcare costs over the conventional procedures. AI-enhanced robotic surgery significantly improves surgical outcomes through higher precision and efficiency, supporting widespread clinical adoption. Despite upfront costs and ethical concerns, continued innovation and integration promise substantial benefits for patient safety and healthcare resource optimization. Future research should focus on long-term patient outcomes and addressing ethical and training challenges.
Artificial intelligence (AI) integrated with robotic systems is transforming oncologic surgery by significantly improving precision, safety, and personalization. The review critically explores the current landscape of AI-powered robotic technologies in tumor resection across various specialties, including urology, neurosurgery, orthopedics, pediatrics, and head and neck oncology. Despite rapid advancements, challenges remain in tumor boundary detection, real-time intraoperative navigation, motion compensation, and seamless data integration. Drawing on evidence from 22 recent clinical studies, pilot trials, and simulation-based research, the review identifies key innovations such as image-free robotic palpation, sensor-assisted feedback, 3D anatomical modeling, and adaptive motion management in radiotherapy. These technologies contribute to enhanced surgical accuracy, reduced invasiveness, and improved intraoperative decision-making. However, barriers such as inconsistent clinical protocols, limited cost-effectiveness data, and variability in performance across tumor types continue to hinder widespread adoption. Challenges persist in complex fields such as pediatric and neurosurgical oncology, where anatomical variability and safety concerns demand more advanced solutions. The review emphasizes the need for interoperable AI-robotic platforms, robust real-time analytics, and standardized safety frameworks. It also highlights the importance of ethical governance and clinician training in ensuring responsible implementation. In conclusion, AI-powered robotic surgery represents a major shift in oncology, offering the potential to improve long-term outcomes and reduce recurrence through data-driven, minimally invasive interventions. Realizing the potential will require interdisciplinary collaboration, longitudinal clinical validation, and strategic integration into healthcare systems.
The review highlights the transformative role of Artificial Intelligence (AI) and machine learning in advancing 3D food printing (3DFP), focusing on improved customization, print quality, consistency, and operational efficiency. While progress has been made, full AI integration across the 3DFP workflow particularly in real-time monitoring and adaptive manufacturing remains limited. The study emphasizes AI’s potential to reduce food waste, enable personalized nutrition, and enhance 3D/4D printing through smart materials and optimized ink formulations. Using the PRISMA framework, recent studies were analyzed to show how AI-driven techniques support print parameter optimization, material behavior prediction, and real-time feedback. Reinforcement learning, for example, can reduce material waste by up to 25%, especially with high-cost or sustainable ingredients. Innovations such as nanomaterials and 4D food printing are expanding applications into areas like personalized healthcare. Despite these advancements, challenges persist in printability, data processing, material compatibility, and AI reliability. The review underscores the need for standardized datasets, biocompatible materials, and regulatory clarity. Future directions include developing intelligent closed-loop systems and fostering interdisciplinary collaboration to improve scalability and robustness. Overall, AIenhanced 3DFP shows strong potential to revolutionize food systems by delivering customized, sustainable, and nutritionally precise solutions.
Structure Equation Modeling (SEM) is a well-known research technique. Before proceed further in data analysis, the researcher describes the fundamentals of Structure Equation Modeling (SEM), as well as its modeling criteria, assumptions, and concepts.
The study utilizes structural equation modeling to examine issues related to normality, missing data, and sampling errors in digital marketing engagement research. The primary focus is on exploring relationships between self-esteem, social comparison, social interactions, perceived social support, and psychological well-being, with perceived social support as a mediating factor. Confirmatory factor analysis is applied to evaluate model fit using data from 400 social media users. Skewness and Kurtosis values are assessed to ensure normality, with scores kept within the acceptable range of -2 to +2. Questionnaires with over 30% missing values are excluded to maintain data quality, and the “10-times rule” is used to ensure adequate sample size and reduce sampling errors. Results confirm a normal distribution and indicate that the model aligns with SEM assumptions, meeting all fit indices. The research offers insights into SEM's application in digital marketing and suggests future studies should investigate advanced modeling techniques for further exploration.
<sec> <title>BACKGROUND</title> This study explores the intricate interplay between e-health and psychological well-being, aiming to uncover the multifaceted impact of its engagement on individuals' psychological well-being. </sec> <sec> <title>OBJECTIVE</title> The study investigates various aspects, including the psychological effects of social media engagement such as response efficacy, response cost, normative beliefs, health motivation and action to e-health to promote positive health and wellness in the digital age. </sec> <sec> <title>METHODS</title> This study used a total sample size of 400 respondents. </sec> <sec> <title>RESULTS</title> The path coefficient analysis (beta-coefficient) for all constructs is positive, indicating that constructs have a positive and highly significant effect on action to health, as both t-values and p-values meet the threshold values. Health motivation partially mediates the relationship between response efficacy and action to e-health since variance accounted for ranges from 20% to 80%. Meanwhile, health motivation also partially mediates both response cost and normative beliefs on action to e-health, with a variance of 37.71% and 32.11% respectively. </sec> <sec> <title>CONCLUSIONS</title> All hypothesizes are positives and accept the research findings that by enhancing the psychological factors through e-health technologies may improve action to e-health. </sec> <sec> <title>CLINICALTRIAL</title> Nil </sec>
This systematic review investigates the transformative impact of artificial intelligence (AI) and financial technology (FinTech) innovations on small and medium-sized enterprise (SME) financing, with a focus on enhancing transparency, efficiency, and financial inclusion. Despite the significant potential of AI and FinTech, substantial gaps remain in understanding their cross-regional and cross-industry effects, as well as in addressing persistent challenges such as AI adoption barriers, regulatory constraints, and decentralized data integration. The review synthesizes findings from peer-reviewed articles published from 2024 onward, sourced from Scopus and Web of Science databases, and examines the role of AI-driven solutions and digital financial platforms in SME financing. Results indicate that AI applications in risk assessment and credit scoring have reduced processing times by approximately 40% and improved loan approval rates by 25%. FinTech innovations have contributed to a 30% increase in financial inclusion, particularly among underserved SMEs in emerging economies. However, critical challenges, including data privacy concerns and limited technological infrastructure, continue to hinder broader adoption. This study contributes to the existing body of knowledge by systematically highlighting the role of AI and FinTech in enhancing SME financial performance and by providing actionable insights for policymakers, financial institutions, and entrepreneurs. The findings underscore the need for future research to address adoption barriers and to conduct cross-country comparative studies. Limitations include the exclusive focus on English-language, peer-reviewed sources, which may restrict the generalizability of the conclusions. Further investigations are recommended to explore the long-term impact of AI and FinTech innovations on SME sustainability and the evolution of regulatory frameworks supporting their implementation.
The integration of artificial intelligence (AI) with wearable technologies is transforming healthcare by enabling real-time health monitoring and predictive analytics. While these advancements enhance personalized health management, challenges such as data privacy, limited personalization, and clinical integration persist. This review evaluates the current state of AI-driven wearables in healthcare, with a particular focus on their applications in diabetes management, cardiovascular health, and elderly care. The novelty of this study lies in its comprehensive analysis of AI algorithms utilized in wearables across diverse health domains. A systematic review was conducted to examine high-quality, peer-reviewed studies published between 2022 and 2024. A comprehensive database search yielded 164 records, with 21 studies meeting the inclusion criteria. These studies provided both quantitative and qualitative insights into the clinical applications of AI-powered wearables for chronic disease management. Data extraction focuses on study characteristics, wearable technologies, health applications, and existing challenges. Findings indicate that AI integration enhances personalized health monitoring, facilitates early disease detection, and supports proactive health management, ultimately improving patient outcomes and reducing strain on healthcare systems. However, issues related to data accuracy, system interoperability, and user acceptance remain critical barriers to widespread adoption. AI-powered wearables demonstrate significant potential in preventive healthcare, chronic disease management, and personalized medicine. As technology advances, these devices are expected to offer more sophisticated diagnostic capabilities and adaptive health interventions. Future research should address challenges such as device accuracy, ethical concerns, and data security while exploring AI applications in mental health and remote patient monitoring. Additionally, longitudinal studies and real-world implementations will be essential to fully integrate AI wearables into mainstream healthcare and maximize their impact on patient care.
Purpose This study aims to investigate social media engagement in private health care. The independent variable is perceived benefits, the mediating variable is consumer innovativeness and the dependent variable is health-protective behaviors. Design/methodology/approach Conducted with 400 participants using purposive sampling due to pandemic restrictions, data were collected through online surveys using validated Likert scales. Statistical analyses, including partial least squares structural equation modeling, were used to assess the relationships, ensuring reliability and validity throughout the research process while upholding ethical standards, such as participant confidentiality and informed consent. Findings The results indicated that social media engagement in private health care has a significant positive relationship with perceived benefits, consumer innovativeness and health-protective behaviors. Furthermore, consumer innovativeness partially mediates the relationship between perceived benefits and health-protective behaviors. Research limitations/implications The discussion highlights the necessity for health-care providers to leverage social media effectively to communicate the value of their services. However, this study acknowledges limitations, including a narrow sample size and the effects of pandemic on participant interactions. Future research should broaden geographic scope, examine demographic differences and assess the role of personality traits in CI. Practical implications The study contributes to existing literature by emphasizing the essential role of perceived benefits and consumer innovativeness in fostering innovative consumer behaviors. It suggests actionable strategies for health-care providers to enhance consumer engagement and promote health-protective behaviors through effective social media use. This study offers valuable insights for improving health-care communication and delivery in the evolving digital landscape. Originality/value This research explores the pivotal role of consumer innovativeness in bridging perceived benefits and health-protective behaviors within the health-care sector, particularly through digital marketing. By examining how innovative consumers perceive and react to health-related information online, the study highlights the potential for digital marketing to effectively promote healthier lifestyles. Consumer innovativeness acts as a catalyst, enhancing the impact of perceived benefits on health-protective behaviors. This underscores the importance for health-care marketers to target and engage innovative consumers, leveraging their openness to new ideas and technologies to foster widespread adoption of health-promoting behaviors, ultimately improving public health outcomes.
The contemporary workplace has become a vital platform for addressing mental health issues, given the global rise in mental health concerns among employees. The study explores how employees perceive mental health within their work environments, pinpointing systemic challenges and identifying areas for potential improvement. The primary goal is to understand diverse employee perspectives on workplace dynamics, emphasizing factors such as workload, work-life balance, organizational culture, and interpersonal relationships. Using a qualitative research approach, ten semi-structured interviews were conducted with employees aged 20 to 52, allowing for in-depth exploration of their unique experiences and views. Findings reveal that most employees perceive their workload as manageable. However, younger employees often struggle to disconnect from work, indicating a generational divide in expectations around work-life balance. Additionally, while participants generally appreciated supportive collegial relationships, they also called for more structured mental health initiatives and clearer communication on mental health resources and policies. The research contributes to existing literature by underscoring the importance of open dialogue on mental health and the need for flexible work arrangements to support employee well-being. Limitations include a relatively small and homogenous sample, which may not capture the full spectrum of workforce experiences. Future research should investigate the impact of hybrid work models on mental health and delve into how organizational policies affect employee well-being in different work settings. Ultimately, creating a mentally healthy workplace benefits organizations by fostering employee satisfaction, productivity, and retention factors crucial in today’s competitive landscape. The study emphasizes that prioritizing mental health through systemic organizational changes and proactive support mechanisms is essential. By nurturing environments that support employee well-being and resilience, organizations can contribute to a healthier, more engaged workforce, helping employees navigate mental health challenges and maintain a balanced life.
Financial institutions are increasingly facing complex challenges in risk management, as traditional methods struggle to predict and mitigate evolving threats in the financial markets. With the rapid pace of change and emerging risks such as economic downturns and cybersecurity issues, the need for advanced, data-driven tools has never been more critical. The novelty of this review presents a comprehensive analysis of AI-driven predictive analytics in financial risk management, offering a unique synthesis of recent advancements in credit risk assessment, fraud detection, and market prediction. Artificial intelligence (AI) and predictive analytics offer a promising solution by enhancing risk forecasting and optimizing decision-making processes. This review explores how AI-driven predictive analytics are transforming risk management into the financial sector, with a particular focus on improving credit risk management, fraud detection, and market predictions. By synthesizing the latest research, the review highlights the integration of machine learning, data mining, and real-time predictive modeling as key innovations reshaping traditional risk assessment methods. A qualitative analysis of recent studies and case reports reveals that AI techniques have significantly improved the accuracy of credit risk assessments and fraud detection, offering financial institutions real-time tools that enhance decision-making efficiency. The findings demonstrate that AI-powered predictive analytics provide more precise, data-driven insights, enabling financial institutions to proactively address potential risks before they escalate. These technologies contribute to better risk mitigation, investment optimization, and enhanced customer trust. However, challenges such as data privacy concerns, algorithm transparency, and the integration of AI into existing systems remain. Future research should focus on overcoming these barriers and further exploring AI’s potential across various financial domains, with an emphasis on improving transparency and tackling integration challenges to maximize its effectiveness in financial risk management.
The rapid growth of eHealth technologies has transformed global healthcare delivery, enhancing patient care, access, and efficiency, particularly in underserved regions. This review synthesizes studies on AI-driven diagnostics and telemedicine, highlighting their potential impact on healthcare systems. Despite these advancements, challenges such as data privacy, ethical issues, and infrastructural barriers remain, along with global disparities in eHealth adoption. The review adopts a systematic approach, analyzing studies from regions like Tanzania, Poland, Spain, and Malaysia, offering a global perspective on digital health innovations. The systematic review analyzed AI-driven eHealth technologies by applying rigorous eligibility criteria, focusing on study design, geographical diversity, technological innovations, and measurable outcomes. It selected peer-reviewed articles from 2024, emphasizing studies on diagnostics, IoT integrations, and mental health. The selection process included studies from both developed and developing regions, ensuring global perspectives. Data extraction and thematic analysis identified key themes such as AI applications, global insights, challenges, opportunities, and ethical considerations, providing a comprehensive synthesis of AI’s transformative impact on healthcare delivery. Notably, it examines the integration of AI, IoT, and the intersection of eHealth with environmental sustainability. Findings show that AI improves diagnostic accuracy and patient outcomes, while IoT and edge computing enhance real-time data processing, especially in remote monitoring and telemedicine. Teleconsultations further contribute to sustainability by reducing travel. However, data privacy and ethical concerns highlight the need for strong regulatory frameworks. The review concludes that eHealth technologies hold transformative potential, but secure, ethical, and equitable implementation is crucial. Implications include enhanced healthcare access, efficiency, and environmental benefits. Limitations involve infrastructural disparities and data governance issues. Future research should focus on scalable, secure eHealth models and address ethical challenges surrounding AI to ensure sustainable, equitable healthcare development.
This research review presents a comprehensive examination of the intricate relationships between social media engagement, consumer innovativeness, and psychological constructs within the private healthcare sector in SDGs, with particular emphasis on the behavioral shifts during the pandemic. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from 400 respondents, the research investigates the complex interplay between key psychological drivers self-efficacy, perceived benefits, and behavioral beliefs and their collective influence on health-protective behaviors. The findings underscore the pivotal role of social media engagement in shaping health-related actions, revealing that consumer innovativeness partially mediates these relationships. Notably, behavioral beliefs emerged as the most significant predictor of health-protective behaviors, followed by self-efficacy and perceived benefits, highlighting the nuanced impact of cognitive factors on digital health engagement. The mediating role of consumer innovativeness underscores the necessity for healthcare marketers to foster innovative consumer mindsets to drive the adoption of health-promoting behaviors through social media channels. This study offers crucial theoretical insights into the evolving role of digital marketing in healthcare and provides practical recommendations for healthcare providers seeking to enhance patient engagement and health outcomes through strategic social media use. By identifying the mechanisms through which psychological elements interact with digital platforms, this research contributes to the broader discourse on optimizing digital communication for health promotion in the post-pandemic era.
Artificial Intelligence (AI) is transforming language learning by offering personalized, adaptive, and emotionally responsive educational experiences. This review synthesizes findings from 26 recent empirical and theoretical studies to evaluate the effectiveness of AI tools such as chatbots, pedagogical agents, and generative AI in enhancing learner engagement, reducing foreign language anxiety, and improving vocabulary acquisition. The results indicate that AI-driven systems contribute to better vocabulary retention, emotional regulation, and learner motivation, particularly when informed by educational theories like self-determination and design thinking. Despite these benefits, the review identifies significant challenges, including digital inequality, insufficient teacher training, algorithmic bias, and a limited linguistic range. While AI can promote learner autonomy and provide low anxiety learning environments, it may also lead to technostress and dependency if not properly integrated with pedagogical support. The study highlights the importance of educator preparedness and ethical AI implementation. Using qualitative-comparative and bibliometric analysis, the review proposes a multidimensional model that emphasizes adaptive feedback, emotional scaffolding, and theoretical alignment. It calls for inclusive AI design, equitable access to technology, and continuous professional development for educators. Future research should adopt longitudinal, interdisciplinary, and culturally adaptive frameworks to examine AI's long-term and sustainable impact on language acquisition in varied educational settings.
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