Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers.

Purpose: It would not be an exaggeration to say that healthcare is the most crucial one in today's perspective. The healthcare sector, in general, is engaged in working on various dimensions simultaneously like the safety, care, quality and cost of services, etc. Still, the desired outcomes from this sector are far away, and it becomes pertinent to address all such issues associated with healthcare on a priority basis for sustaining the outcomes in a long-term perspective. The present study aims to explore the healthcare sector and list out the directly associated enablers contributing to increasing the viability of the healthcare sector. Besides, the interrelationship among the enlisted enablers needs to be studied, which further helps in setting-out the priority to deal with individual enablers based on their impedance in the contribution towards viability increment.Design/methodology/approach: The authors have done an extensive review to list out the enablers of the healthcare sector to perform efficiently and effectively. Further, the attempt has been made on the enablers to rank them by using the modified Total Interpretative Structure Modelling (m-TISM) approach. The validation of the study reveals the importance of enablers based on their position in the hierarchical structure. Further, the MICMAC analysis on the identified enabler is performed to categorize the identified enablers in the different clusters based on their driving power and dependence.Findings: The research tries to envisage the importance of the healthcare sector and its contribution towards national development. The outcomes of the m-TISM model in the present study reveal the noteworthy contribution of the organizational structure in managing the healthcare facilities and represented it as the perspective of future growth. The well-designed organizational structure in the healthcare industry helps in establishing better employee–employer cooperation, workforce coordination and inter-department cooperation.Research limitations/implications: Every research work has limitations. Likewise, the present research work also has limitations, i.e. input taken for developing the models are from very few experts that may not reflect the opinion of the whole sector.Practical implications: The healthcare sector is the growing sector in the present-day scenario, and it is essential to keep the quality of treatment in check along with the quantity. The present study has laid down the practical foundations for improvement in the healthcare sector viability. Besides, the study emphasized on accountability of the healthcare sector officials to go with the enablers having the strong driving power for effective utilization of all the resources. This would further help them in customer (patients) satisfaction.Originality/value: Despite an increase in demand for good quality healthcare facilities worldwide, the growth of this sector is bounded by the economic, demographic, cultural and environmental concerns, etc. The present study proposed a unique framework that provides a better understanding of the enablers. It would further help in playing a key role in increasing the viability of the healthcare sector. The hierarchy developed with the help of m-TISM and MICMAC analysis will help the viewers to recognize the important enablers based on their contribution to the viability improvement of the healthcare sector.

Digital marketing has become a game-changer by combining cutting-edge technologies, insights into how customers behave, and applicability across industries to change how businesses plan and how they interact with customers. Digital marketing is a key part of being competitive, sustainable, and innovative in a world where more and more people are using the internet and social media. Even though this subject is important, the study of it is still scattered, which shows that there is a need to systematically map out its intellectual structure. This research utilizes a bibliometric and topic modeling methodology, analyzing 4722 publications sourced from the Scopus database, including the string “Digital Marketing”. The authors employed Latent Dirichlet Allocation (LDA), a method from Natural Language Processing, to discern latent study themes and Vosviewer 1.6.20 for bibliometric analysis. The results explore ten main thematic clusters, such as digital marketing and blockchain, applications in the health and food industries, higher education and skill enhancement, machine learning and analytics, small and medium-sized enterprises (SMEs) and sustainability, emerging trends and ethics, sales transformation, tourism and hospitality, digital media and audience perception, and consumer satisfaction through service quality. These clusters show that digital marketing is becoming more interdisciplinary and is becoming more connected to ethical and technological issues. The report finds that digital marketing research is changing quickly because of artificial intelligence (AI), blockchain, immersive technology, and reflect it with a digital business environment. Future directions encompass the expansion of analyses to new economies, the implementation of advanced semantic models, and the navigation of ethical difficulties, thereby guaranteeing that digital marketing fosters both business progress and public welfare.