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Artificial intelligence (AI)-based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of AI in this field. This study aimed to evaluate the global development of scientific publications and constructed interdisciplinary research topics on the theory and practice of AI in medicine from 1977 to 2018. We obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the research landscape. The applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources. The application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries.

Background This study examines the impact of Artificial Intelligence (AI) on maternal mortality in alignment with Sustainable Development Goal (SDG) 3.1, which aims to reduce maternal mortality to below 70 per 100,000 live births by 2030. Despite advancements, maternal mortality remains disproportionately high in developing countries due to weaker healthcare infrastructure. Methods Using panel data from 70 countries (1990–2022), sourced from WHO’s Global Burden of Disease (GBD), World Bank’s World Development Indicators (WDI), UNCTAD, and the World Robotics database, we apply the Difference-in-Differences (DiD) approach to assess AI’s impact over time and the Auto-Regressive Distributed Lag (ARDL) model to examine short- and long-term effects. Results AI adoption significantly reduces maternal mortality, particularly in developing countries, where post-2000 advancements have led to notable declines. ARDL results show that 27% of deviations from long-term maternal mortality trends are corrected annually, highlighting AI’s sustained impact. The DiD analysis indicates AI’s greatest benefits in resource-limited settings, including improving early diagnostics, personalized care, and remote monitoring. In developed countries, AI’s effects are marginal due to existing advanced healthcare systems. Conclusion AI presents a transformative solution for reducing maternal mortality, particularly in low-resource settings. Policymakers should prioritize AI-driven healthcare, expand digital infrastructure, and ensure equitable access to maximize its benefits. AI integration is crucial for addressing maternal health disparities and accelerating progress toward SDG 3.1.

Accurate and robust forecasting of Emergency Medical Services (EMS) demand is crucial for ensuring timely ambulance dispatch and efficient resource allocation, particularly in low-resource public health systems, such as those in India. While most prior EMS forecasting studies have focused on urban settings in developed countries with rich, granular data, limited research has explored district-level forecasting using real-world ambulance dispatch data from India. Moreover, existing models often trade off robustness for accuracy or rely on complex black-box architectures, limiting their interpretability and real-world deployment. This study examines whether a heterogeneous ensemble of interpretable and complementary learners can outperform traditional and state-of-the-art regressors for district-level EMS forecasting, utilizing limited real-world features. To address this challenge, we propose EM-LR (Ensembled Meta-Learner with Linear Regression), a meta-learning framework that integrates four diverse base models-Lasso Regression, Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGB)-via a linear regression meta-learner. Unlike prior meta-learners that stack similar tree-based or linear models, EM-LR combines low-variance, diverse learners to enhance robustness while maintaining model interpretability through SHAP-based feature analysis and transparent ensemble weights. Using only temporal and meteorological inputs, EM-LR forecasts daily EMS call volumes across five diverse districts in the state of Uttar Pradesh. We benchmark EM-LR against traditional models and recent advanced variants, including Twin Bounded Least Squares Support Vector Regression (TBLSSVR), Asymmetric-Huber based Extreme Learning Machine (AHELM), and Mexican-Hat Kernelized Large Margin Distribution Machine-based Regression (MHKLDMR), demonstrating superior accuracy and reduced prediction variance. Experimental results show up to 9.5% reduction in RMSE and over 40% variance reduction. EM-LR thus offers a scalable and interpretable forecasting solution tailored to the operational constraints of developing public health systems, supporting data-driven emergency planning and equitable healthcare delivery.

Development aid is recognized in the fields of international relations and development economics as an important geopolitical tool for supporting the outward FDI of developed countries in developing countries. However, little attention has been given to the role of firm heterogeneity, a central concept in IB, in the aid–FDI relationship. This study helps address this gap by bringing the aid–FDI nexus to the level of the firm. We argue that both infrastructure and non-infrastructure aid encourage private home firms’ FDI entries, even when unrelated to aid project execution. We propose that developed-country firms that are less able to manage host-country challenges or have access to the home state should be more sensitive to both types of aid. Analyzing data from 1451 private Japanese firms in 76 developing countries (1991–2002), we find that both types of aid raise the FDI entry likelihood. However, the firm-level contingencies are confirmed only for the infrastructure aid–FDI nexus. Non-infrastructure aid seems to mitigate market and political uncertainties that home firms are less capable of tackling on their own. This study complements research in IB that focuses on Chinese infrastructure aid and emphasizes infrastructure-related FDI and political expansion motives of SOEs in the aid–FDI nexus.

Global demographic transitions driven by declining fertility and increasing longevity are reshaping population age structures, with significant implications for health systems, economic development and social stability. This perspective article examines the concept of the demographic dividend and the broader consequences of population change across countries at different stages of demographic transition. The analysis describes how reductions in fertility can lower dependency ratios and create opportunities for economic growth, poverty reduction and improved well-being, emphasizing that the demographic dividend represents a potential rather than an automatic outcome. Realizing these gains requires coordinated, cross-sectoral strategies and sustained investments in health, education, infrastructure and governance. The article explores policy priorities tailored to different demographic contexts, including strengthening reproductive health services, promoting female education and labour force participation, and investing in human capital. It also addresses the challenges associated with population ageing, highlighting the need to extend working lives, support healthy ageing, expand long-term care systems and foster technological innovation to sustain productivity. Intended for policy-makers and public health stakeholders, it underscores the importance of integrated, life-course approaches to maximize the benefits of demographic change and mitigate its risks.

In developed countries, a significant gap persists in the absence of empirical, survey-based rubrics to measure AI's technical characteristics and predictive analytics in the public service sector. This study fills this gap by developing and validating survey-based rubrics through a comparison between Canada and the United States (US). Following MacKenzie, Shiau, and Huang's scale development procedures, this research utilized data from Canadian and US government AI websites twice, employing structural equation modeling (SEM) and PLS methods to examine the scale properties and test the relationships between AI's technical characteristics and predictive analytics. Findings show that supervised and unsupervised machine learning, along with deep learning, are positively associated with predictive analytics across public service sectors in both countries. Conversely, artificial neural networks are not positively associated with predictive analytics in Canada, whereas they are in the US. The relationship between artificial neural networks and predictive analytics varies across countries.

Recently clinicians have become more reliant on technologies such as artificial intelligence (AI) and machine learning (ML) for effective and accurate diagnosis and prognosis of diseases, especially mental health disorders. These remarks, however, apply primarily to Europe, the USA, China and other technologically developed nations. Africa is yet to leverage the potential applications of AI and ML within the medical space. Sub-Saharan African countries are currently disadvantaged economically and infrastructure-wise. Yet precisely, these circumstances create significant opportunities for the deployment of medical AI, which has already been deployed in some places in the continent. However, while AI and ML have come with enormous promises in Africa, there are still challenges when it comes to successfully applying AI and ML designed elsewhere within the African context, especially in diagnosing mental health disorders. We argue, in this paper, that there ought not to be a homogeneous/generic design of AI and ML used in diagnosing mental health disorders. Our claim is grounded on the premise that mental health disorders cannot be diagnosed solely on ‘factual evidence’ but on both factual evidence and value-laden judgements of what constitutes mental health disorders in sub-Saharan Africa. For ML to play a successful role in diagnosing mental health disorders in sub-Saharan African medical spaces, with a precise focus on South Africa, we allude that it ought to understand what sub-Saharan Africans consider as mental health disorders, that is, the value-laden judgements of some conditions.

Lack of expertise, among other reasons, has been cited as the reason for Nigerian libraries’ sluggish adoption and application of innovative technology. To solve this problem, a thorough analysis of the level of technology implementation in Nigerian libraries is needed. This work closes the gap in the literature by using the PRISMA search method to examine 42 pertinent works published between 2016 and 2023. A questionnaire was used to collect data from 240 respondents using the survey approach. Six federal university libraries were selected by a stratified random sampling procedure, one from each of Nigeria’s six geopolitical zones. Then, 40 library employees from each of the selected libraries were selected using a purposive sample technique. Mean and standard deviation were used to analyze the responses. The results demonstrate a grand mean of 2.44 (SD=1.15), indicating low staff awareness of the potential benefits of innovative technology for library operations. A negative grand mean of 2.42 on the degree of usage was also noted, indicating low use of cutting-edge technology in the research area. A lack of internet penetration, a deficiency of digital literacy, insufficient funds, and unstable power sources were among the problems found limiting Nigerian libraries from taking advantage of advanced technologies. In light of this, the study suggests that the Nigerian government should increase budget allocations for libraries and enhance policies that support the self-development of library staff. Additionally, libraries in developing countries should seek mentorship from those in developed nations to update their knowledge and skills, enabling better implementation and deployment of cutting-edge technology in their libraries.

Direct-to-consumer (DTC) health care artificial intelligence (AI) apps hold the potential to bridge the spatial and temporal disparities in health care resources, but they also come with individual and societal risks due to AI errors. Furthermore, the manner in which consumers interact directly with health care AI is reshaping traditional physician-patient relationships. However, the academic community lacks a systematic comprehension of the research overview for such apps. This paper systematically delineated and analyzed the characteristics of included studies, identified existing barriers and design recommendations for DTC health care AI apps mentioned in the literature and also provided a reference for future design and development. This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines and was conducted according to Arksey and O'Malley's 5-stage framework. Peer-reviewed papers on DTC health care AI apps published until March 27, 2023, in Web of Science, Scopus, the ACM Digital Library, IEEE Xplore, PubMed, and Google Scholar were included. The papers were analyzed using Braun and Clarke's reflective thematic analysis approach. Of the 2898 papers retrieved, 32 (1.1%) covering this emerging field were included. The included papers were recently published (2018-2023), and most (23/32, 72%) were from developed countries. The medical field was mostly general practice (8/32, 25%). In terms of users and functionalities, some apps were designed solely for single-consumer groups (24/32, 75%), offering disease diagnosis (14/32, 44%), health self-management (8/32, 25%), and health care information inquiry (4/32, 13%). Other apps connected to physicians (5/32, 16%), family members (1/32, 3%), nursing staff (1/32, 3%), and health care departments (2/32, 6%), generally to alert these groups to abnormal conditions of consumer users. In addition, 8 barriers and 6 design recommendations related to DTC health care AI apps were identified. Some more subtle obstacles that are particularly worth noting and corresponding design recommendations in consumer-facing health care AI systems, including enhancing human-centered explainability, establishing calibrated trust and addressing overtrust, demonstrating empathy in AI, improving the specialization of consumer-grade products, and expanding the diversity of the test population, were further discussed. The booming DTC health care AI apps present both risks and opportunities, which highlights the need to explore their current status. This paper systematically summarized and sorted the characteristics of the included studies, identified existing barriers faced by, and made future design recommendations for such apps. To the best of our knowledge, this is the first study to systematically summarize and categorize academic research on these apps. Future studies conducting the design and development of such systems could refer to the results of this study, which is crucial to improve the health care services provided by DTC health care AI apps.