Journal of Digital Health

Research trends in telemedicine for chronic diseases: A bibliometric study

Chaoqun Dong — 2025-08-20

Objective: The aim of this bibliometric study review is to analyze the current research status, hotspots, and trends in telemedicine in the management of chronic diseases using VOSviewer and CiteSpace. Methods: Relevant publications were retrieved from the Web of Science Core Collection and Scopus databases up to the year 2023. The search strategy was based on keywords such as "telemedicine", "chronic disease" and "management" as well as their synonyms and related terms. Boolean operators were applied to combine these terms appropriately. The literature was manually screened. CiteSpace and VOSviewer were used for visual analysis. The knowledge map was created from multiple dimensions, including publication trends, author collaboration, country distribution, keyword co-occurrence, clustering and burst analysis. Results: A total of 1,747 articles were included. The annual number of publications showed an upward trend, peaking in 2021 with 210 articles. The United States, China, and Italy were the leading countries in publication volume, with the United States contributing the most (37.71%). The most prolific authors were Zanaboni P., and Koehler F., who published nine papers each. Collaboration among authors appeared to be relatively independent. The clustering of keywords revealed three main themes: diseases, management pathways, and clinical applications. The co-occurrence analysis showed that the main focuses were "chronic disease identification", "disease management pathways", and "clinical application of telemedicine". Technology-related keywords such as "mobile applications", "telemedicine", and "digital health" occurred frequently. The burst analysis indicated that "coronavirus disease 2019" and "digital health" have emerged as current research hotspots. Conclusion: Research on telemedicine in chronic disease management is growing rapidly. The focus has shifted from traditional healthcare services to intelligent technologies and personalized care. Technologies such as mobile health, digital interventions, and remote monitoring are expected to play key roles in self-management, long-term monitoring, and improving accessibility to healthcare. Future research should emphasize interdisciplinary integration and patient adherence to promote practical applications across the full chronic disease lifecycle.

Learning from the pandemic: A critical review of public health system evolution in China, India, and Japan

Tomo Kawane — 2025-08-19

The COVID-19 pandemic has highlighted both the strengths and vulnerabilities of global public health systems (PHSs) and international cooperation. To identify key strategies for enhancing global health security, this research seeks to reveal crucial factors needed for resilient and integrated global health systems. In the absence of overseeing agencies in Asia, constructive policy recommendations for respective governments are required, with a combination of centralized policy directives and localized implementation strategies being the key. The sheer demographic size of China and India, shared challenges of depopulation of China and Japan, and the current or future issue of aging between China, India and Japan, make it necessary to have a comparative analysis of public health responses in China, India, and Japan. The novelty of the study lies in this comparative study on the evolution of PHSs in these countries since the 1990s using the WHO health indicators and their COVID-19 responses. Political economy must ensure public trust through equitable access to health care and transparent communication, while balancing public health needs with economic imperatives. The findings underscore the need for coordinated governance, preventive healthcare and livelihood efforts to prepare for future health crises and identify directions for international collaborative efforts to combat them.

Enhancing patient autonomy in data ownership: privacy models and consent frameworks for healthcare

Minal R. Narkhede — 2025-03-03

Patient autonomy in healthcare has become increasingly significant in the digital age as individuals seek greater control over their health data. This review examines the ethical, legal and technological aspects of patient data ownership, emphasizing the need for privacy models and consent frameworks to empower patients, safeguard privacy and enhance transparency. Traditional doctor-patient confidentiality faces challenges due to advancements such as electronic health records, artificial intelligence and wearable technologies, necessitating updated frameworks to protect patient rights. Privacy models such as private, public and hybrid models present varying implications for data control, security and societal benefits. Emerging technologies such as blockchain and AI are revolutionizing data privacy by decentralizing data storage and enabling patient control while ensuring secure and ethical data utilization. Advanced consent frameworks, including dynamic and granular consent, provide patients with flexibility and transparency and promote trust and active participation in data-sharing decisions. Real-world implementations, such as Australia’s My Health Record and Estonia’s e-Health system, demonstrate the potential of patient-centric privacy frameworks to enhance healthcare quality and innovation. However, significant challenges persist, including regulatory ambiguities, cybersecurity risks and gaps in digital literacy. Addressing these issues requires collaboration among stakeholders to develop adaptable, secure and interoperable systems that prioritize patient autonomy. By integrating patient education, fostering interoperability and leveraging adaptive technologies, healthcare systems can balance privacy and innovation, build trust and ensure ethical data practices that empower individuals while advancing public health objectives.

Innovation management in AI advancement: Revolutionizing health system and biopharma

Sabeen Khaliq — 2025-07-22

The integration of advanced technologies is reshaping the healthcare and biopharma industries and requires effective innovation management frameworks to leverage their potential while fully addressing associated risks. This literature review explores the global landscape of AI-driven innovation in the healthcare and biopharma, examining strategies, challenges and future directions. Technological applications are revolutionizing the pharmaceutical value chain by accelerating drug discovery and development, optimizing manufacturing processes and enhancing patient care delivery. For instance, AI-assisted drug development has reduced the time for candidate identification from years to just a few months, as demonstrated by the DSP-1181 project. Healthcare organizations employ statistical and symbolic methods to improve operational efficiency and patient outcomes. Despite promising advancements, significant challenges persist, including regulatory approval complexities, transparency and validation of algorithms, intellectual property protection, and ethical concerns such as data accuracy and bias. The responsibility for autonomous systems introduces additional difficulties in ownership and accountability. Successful adoption requires strategic planning for technology deployment alongside compliance with evolving regulatory and international standards. This review synthesizes current trends while identifying gaps in implementation. It provides structured insights for researchers, practitioners and policymakers.

MedBrain® for making diagnosis of childhood illnesses: a case report

George Uchenna Eleje — 2025-05-09

Diagnosing childhood diseases can be particularly challenging, especially in areas with limited access to specialists. This case report explored the application of MedBrain^®, an artificial intelligence–powered Clinical Decision Support System (CDSS), in supporting diagnosis in paediatric practice. We presented two clinical cases from Nigeria: a 4-year-old girl diagnosed with urinary tract infection and a 3-year-old girl diagnosed with acute gastroenteritis. These cases were selected to demonstrate common but diagnostically challenging paediatric conditions with overlapping symptoms. For each case, MedBrain^®was used prospectively to generate a ranked list of differential diagnoses with corresponding diagnostic confidence scores based on patient symptoms and clinical findings. The system utilized a hybrid algorithm combining rules-based logic and machine learning to assess input data and compute likelihood estimates. In both cases, MedBrain^®'s top-ranked diagnoses— urinary tract infection (96%) and gastroenteritis (95%)—were confirmed by attending paediatricians, validating its clinical utility. Comparatively, the standard clinical diagnosis was initially uncertain in both scenarios due to nonspecific presentations. These findings emphasized the potential of MedBrain^® to augment paediatric diagnostic accuracy, particularly in low-resource or non-specialized settings. Future studies should evaluate MedBrain^® in larger prospective cohorts or randomized control trials and compare its diagnostic performance with established clinical guidelines.