Editorial: Advances in artificial intelligence and robotics in joint arthroplasty

Implementation research (IR) is important for addressing equity in global health. However, there is limited knowledge on how to operationalize IR for health equity, and pathways for improving health equity through IR in global health settings. This paper provides an overview of guidance and frameworks for thinking about health equity as part of IR while noting the gaps in how this guidance and frameworks apply to global health. It proposes an approach to guide implementation teams in the application of IR for achieving equity in global health considering these gaps. It describes key equity considerations for different aspects of IR (i.e., implementation contexts, strategies, outcomes, and research designs). These considerations can be applied prospectively and retrospectively, and at different stages of IR. The paper further describes causal pathways, intervention levers, and strategies for achieving health equity in global health settings through IR. Central to these pathways is the power asymmetries among different actors involved in IR in global health and how these contribute to health inequities. The paper suggests recommendations and strategies for shifting the balance of power among these actors while addressing the structural and systemic determinants of health inequities as part of IR. Explicit considerations for health equity as part of implementation research and practice are needed for the achievement of global health goals. Such explicit considerations should look back as much as possible, and entail defining and analyzing health inequities and intervening on the underlying causes and mechanisms of health inequities as part of IR on a routine basis.

Teledermatology in medical student, postgraduate trainee, and global dermatology education: A systematic review

In an era where artificial intelligence (AI) is revolutionizing business paradigms, this study delves into the intricacies of AI-driven business models, offering a nuanced understanding of their emergence, evolution, and impact on traditional business strategies. This scholarly inquiry aims to dissect the role of AI in reshaping business models, highlighting the interplay between technological innovation and business strategy. The study meticulously examines the integration of AI into various business facets by employing a systematic and thematic analysis of a diverse range of literature, including academic journals, industry reports, and case studies. This methodological approach facilitates a comprehensive understanding of AI's role in business innovation, addressing both the opportunities and challenges it presents. The findings reveal that AI-driven business models are characterized by enhanced operational efficiency, data-driven decision-making, and customer-centric approaches. These models signify a transformative shift from conventional business strategies, demanding a reevaluation of leadership roles and ethical considerations in the digital age. The study identifies key challenges in AI implementation, such as technical complexities and ethical dilemmas, while uncovering AI's vast opportunities for business growth and competitive advantage. Conclusively, the study recommends a balanced approach to AI integration, emphasizing the need for ethical AI practices, continuous adaptation, and a synergy between AI capabilities and human insights. It advocates for business leaders to embrace AI not just as a technological tool, but as a catalyst for sustainable and innovative business growth. This scholarly work contributes significantly to the discourse on AI in business, providing a foundational framework for future research and practical application in AI-driven business innovation. Keywords: Artificial Intelligence, Business Models, Digital Transformation, AI Integration, Leadership in AI, Ethical AI Practices.

BackgroundSerious childhood illnesses (SCI), defined as severe pneumonia, severe dehydration, sepsis, and severe malaria, remain major contributors to amenable child mortality worldwide. Inadequate recognition and treatment of SCI are factors that impact child mortality in Botswana. Skills assessments of providers caring for SCI have not been validated in low and middle-income countries.ObjectiveTo establish preliminary inter-rater reliability, validity evidence, and feasibility for an assessment of providers who care for SCI using simulated patients and remote video capture in community clinic settings in Botswana.MethodsThis was a pilot study. Four scenarios were developed via a modified Delphi technique and implemented at primary care clinics in Kweneng, Botswana. Sessions were video captured and independently reviewed. Response process and internal structure analysis utilized intra-class correlation (ICC) and Fleiss’ Kappa. A structured log was utilized for feasibility of remote video capture.ResultsEleven subjects participated. Scenarios of Lower Airway Obstruction (ICC = 0.925, 95%CI 0.695–0.998) and Hypovolemic Shock from Severe Dehydration (ICC = 0.892, 95%CI 0.596–0.997) produced excellent ICC among raters while Lower Respiratory Tract Infection (LRTI, ICC = 0, 95%CI -0.034–0.97) and LRTI + Distributive Shock from Sepsis (0.365, 95%CI -0.025–0.967) were poor. Oxygen therapy (0.707), arranging transport (0.706), and fluid administration (0.701) demonstrated substantial task reliability.ConclusionsInitial development of an assessment tool demonstrates many, but not all, criteria for validity evidence. Some scenarios and tasks demonstrate excellent reliability among raters, but others may be limited by manikin design and study implementation. Remote simulation assessment of some skills by clinic-based providers in global health settings is reliable and feasible.

Artificial intelligence (AI) is affecting the practice of orthopedic surgery, offering innovative solutions from preoperative planning to intraoperative guidance and real-time adaptation. This review highlights current advancements in AI-driven imaging analysis, anatomical segmentation, and implant selection, highlighting improvements in surgical precision, efficiency, and patient-specific customization. Intraoperatively, AI enables real-time image processing, integration with robotic systems, and adaptive feedback mechanisms that enhance accuracy, reduce complications, and personalize care. Clinical applications span joint replacement, spine, and trauma surgery, where AI supports diagnosis and offers support in decision-making, often surpassing conventional methods. Despite these promising developments, challenges remain regarding data quality, model generalizability, transparency, and ethical considerations. Future directions emphasize explainable AI, multimodal data integration, and closer synergy between AI, robotics, and digital health to advance personalized orthopedic care.

This paper uses bibliometric method and knowledge graph visualization technology to analyze the 237 papers of CNKI core journals from 2006 to 2021, from the key words, number of papers, authors, publishing institutions and high-impact journals. Statistical analysis, explaining the research status and research hotspots of artificial intelligence life cycle, and expounding the shortcomings and trends of research. The research on artificial intelligence technology is divided into exploratory stage and development stage according to the annual publication volume. From 2006 to 2015, the research hotspots mainly focus on "artificial intelligence" and " neural network ". From 2016 to 2021, the research hotspots mainly focus on three aspects: "artificial intelligence", "artificial intelligence technology" and "deep learning", and the research is gradually deepened, with a total of 223 journal articles. The research of artificial intelligence technology is in the development stage, and various fields are actively studying artificial intelligence technology, but the existing research focuses on the application level, the deep learning theory is not perfect, the basic technology and basic theory are ignored, and there is a lack of solutions to the problem of privacy leakage. Future research should pay more attention to basic technology and innovative research. There may be broader research space for research from two aspects: "integration and breakthrough of deep learning theory" and "machine learning evolution towards distributed privacy protection".

Artificial Intelligence (AI) is revolutionizing healthcare by augmenting diagnostic accuracy, personalizing treatment plans, and accelerating drug discovery processes. This review synthesizes current literature and case studies to explore AI's multifaceted applications in healthcare. The review begins by examining AI's role in early disease detection, highlighting its use of advanced techniques such as deep learning and neural networks for analyzing medical images with unprecedented precision. Case studies underscore AI's success in detecting cancers and predicting cardiovascular risks, demonstrating significant advancements in diagnostic capabilities. Moving to personalized treatment, the review explores how AI integrates genomic data, electronic health records (EHRs), and lifestyle information to tailor medical interventions. AI-driven predictive analytics enable the forecasting of treatment outcomes and the recommendation of optimal personalized therapies, enhancing patient care and treatment efficacy. In the realm of drug discovery and development, AI algorithms expedite the identification of potential drug targets, optimize molecular designs, and predict clinical trial outcomes. Case examples illustrate AI's effectiveness in discovering novel compounds and repurposing existing drugs, promising accelerated innovation in pharmaceutical research. Ethical considerations, including data privacy and algorithmic bias, are critically analyzed alongside regulatory frameworks like GDPR and FDA guidelines, ensuring responsible AI implementation in healthcare. Ultimately, this review underscores AI's transformative impact on healthcare delivery, offering insights into its potential to reshape medical practices, improve patient outcomes, and pave the way for future advancements in precision medicine and therapeutic innovation.

The consistent practice of digitalization shows the ‘emergence’ of a new quality of work of development institutes, businesses. The further development of the infrastructure, taking into account innovations and ‘digits’, will make it possible to fulfill important tasks in the partial implementation of the directions of progressive digital development of the regions of Ukraine outlined by the government in the post-war period. The purpose of the article is research and data analysis in terms of sub-indices of digital transformation of the regions of Ukraine with the aim of finding opportunities for digitization of infrastructure and working business processes. The authors consider three stages of the development of artificial intelligence through the prism of digitalization of the economy and virtual reality, namely: basic artificial intelligence, universal artificial intelligence, artificial super intelligence. The article attempts to present the opportunities created as a result of industry digital transformation, the development of the Internet, the penetration of basic e-services, the introduction of a paperless regime, and the development of centers for the provision of administrative services. The sub-index ‘Penetration of basic e-services’ shows positive dynamics. Improvement of this sub-index indicates an improvement in terms of connection to the Unified Information System of the Social Sphere; availability in all departments of the State Registration of Civil Status Acts, maternity hospitals e-Malyatko; connecting the Technical Inventory Bureau to the State Register of Property Rights. It was concluded that the new virtual reality in combination with the ever-present application of the latest information and communication technologies is the driving force behind the formation of Industry 5.0, innovations are ‘born’, and new types of digital services appear. The IoT, big data analytics, AI, communicativeness and networking of interactions, diffusion of innovations and Internet technologies become the impetus for digital and innovative changes in all sectors of the economy.

The increasing number of obstetrics and gynecology trainees and providers interested in global health experiences along with shortages of skilled health-care providers in lower resource settings creates important opportunities to incorporate simulation in global health work and in global health settings. Simulation can be used to prepare providers ethically, emotionally, and clinically for global health work. In addition, simulation can be used to augment the teaching of essential skills and introduce comparatively new technology to providers in resource-limited settings. Simulation aimed at lifesaving and quality-of-life measures has the potential to decrease maternal mortality and morbidity as well as deaths related to gynecologic cancers and unsafe abortions in any setting including areas with fewer resources. To ensure relevance and sustainability, factors to consider when adapting simulations and simulation curricula for any environment include making sure the supplies and content are adapted to the needs, culture, and resources of the specific health-care context. It is essential to coordinate with local partners in international settings to design simulation curricula both for preparations prior to international travel and for use in those settings.

Public service provision in the frontline, coined street-level bureaucracy, has been gradually impacted by information and communications technology (ICT) for decades. This impact, however, has mostly considered ICT as a tool suitable for tasks with low complexity. With recent advances in artificial intelligence (AI), there are examples of AI use for more complex street-level work. Examples include cases where AI is used for assessing eligibility for social benefits, predictive policing models, automated grading, and diagnostics in healthcare. While these applications demonstrate potential benefits, they also introduce new challenges related to privacy, accountability, corporatization and alienation of street-level work, and client service experiences. This article is a critical reflection on the street-level potential of AI in providing public services. This study contributes to the ongoing debate on AI's impact in street-level work by emphasizing both the potential benefits and risks associated with AI integration in frontline service provision. While AI may mitigate some limitations of human decision-making (e.g., subjectivity, inconsistency, and bias), it can also introduce challenges that require careful consideration (e.g., lack of transparency, data-driven bias, and limited contextual adaptation). By critically reflecting on AI's street-level potential, this article calls for a balanced approach to AI adoption in street-level work.

Incorporating contextual factors into engineering design processes is recommended to develop solutions that function appropriately in their intended use contexts. In global health settings, failing to tailor solutions to their broader context has led to many product failures. Since prior work has thus far not investigated the use of contextual factors in global health design practice, we conducted semi-structured interviews with 15 experienced global health design practitioners. Our analysis identified 351 instances of participants incorporating contextual factors in their previous design experiences, which we categorized into a taxonomy of contextual factors, including 9 primary and 32 secondary classifications. We summarized and synthesized key patterns within all the identified contextual factor categories. Next, this study presents a descriptive model for incorporating contextual factors developed from our findings, which identifies that participants actively sought contextual information and made conscious decisions to adjust their solutions, target markets and implementation plans to accommodate contextual factors iteratively throughout their design processes. Our findings highlight how participants sometimes conducted formal evaluations while other times they relied on their own experience, the experience of a team member or other stakeholder engagement strategies. The research findings can ultimately inform design practice and engineering pedagogy for global health applications.

This chapter explores the integration of Artificial Intelligence (AI) into Industrial Engineering (IE) education, focusing on foundational concepts, applications, research innovations, and ethics. By embedding AI literacy into curricula using tools like Python, TensorFlow, and simulation software, it fosters skills in predictive analytics, optimization, and AI-driven decision-making. Practical applications are demonstrated through case studies in logistics, healthcare, and sustainable manufacturing, highlighting AI's synergy with Multi-Criteria Decision-Making (MCDM) methods such as AHP and TOPSIS for dynamic system optimization. Adaptive learning tools and AI tutors enhance personalized education, while ethical challenges—algorithmic bias, data privacy, and access gaps—call for governance and transparency. Emerging trends like explainable AI (XAI), digital twins, and human-AI collaboration promise to redefine IE education, equipping students to address global industrial challenges. This synthesis of theory and practice cultivates engineers who balance innovation with ethical responsibility in a rapidly evolving technological landscape.

Resource-poor countries and regions require effective, low-cost diagnostic devices for accurate identification and diagnosis of health conditions. Optical detection technologies used for many types of biological and clinical analysis can play a significant role in addressing this need, but must be sufficiently affordable and portable for use in global health settings. Most current clinical optical imaging technologies are accurate and sensitive, but also expensive and difficult to adapt for use in these settings. These challenges can be mitigated by taking advantage of affordable consumer electronics mobile devices such as webcams, mobile phones, charge-coupled device (CCD) cameras, lasers, and LEDs. Low-cost, portable multi-wavelength fluorescence plate readers have been developed for many applications including detection of microbial toxins such as C. Botulinum A neurotoxin, Shiga toxin, and S. aureus enterotoxin B (SEB), and flow cytometry has been used to detect very low cell concentrations. However, the relatively low sensitivities of these devices limit their clinical utility. We have developed several approaches to improve their sensitivity presented here for webcam based fluorescence detectors, including (1) image stacking to improve signal-to-noise ratios; (2) lasers to enable fluorescence excitation for flow cytometry; and (3) streak imaging to capture the trajectory of a single cell, enabling imaging sensors with high noise levels to detect rare cell events. These approaches can also help to overcome some of the limitations of other low-cost optical detection technologies such as CCD or phone-based detectors (like high noise levels or low sensitivities), and provide for their use in low-cost medical diagnostics in resource-poor settings.

Purpose This study analyzes China’s strategic initiatives in metaverse and artificial intelligence (AI) development, examining their impact on academic research, industry innovation, and policy formulation. It aims to understand how government policies and investments have shaped research agendas and to identify challenges and opportunities in these fields. Design/methodology/approach The research employs a comprehensive analysis of government documents, funding schemes, and research output. It examines key policies, investment programs, and academic publications to track trends in metaverse and AI development in China. The study utilizes bibliometric analysis to assess publication trends, citation patterns, and international collaboration networks. Findings China’s proactive approach, characterized by strong government support and significant private sector investment, has led to a substantial increase in research output and quality in metaverse and AI fields. Chinese institutions have become major contributors to global publications, with growing citation rates and presence at international conferences. The research identifies emerging challenges in privacy, ethical AI development, and digital divide concerns. Practical implications The findings provide insights for policymakers, researchers, and industry stakeholders on the development trajectory of metaverse and AI technologies in China. They highlight the need for balanced approaches to innovation, regulation, and ethical considerations in these rapidly evolving fields. Social implications The study underscores the potential of metaverse and AI technologies to transform various sectors of society, from education and healthcare to entertainment and social interactions. It emphasizes the importance of addressing digital equity and ethical AI deployment to ensure broad societal benefits. Originality/value This research offers a comprehensive overview of China’s approach to metaverse and AI development, providing a unique perspective on the interplay between government initiatives, academic research, and industry innovation. It contributes to the broader discussion on the global development of these transformative technologies and their implications for future technological landscapes.

Digital health and artificial intelligence technologies such as telemedicine, mobile health applications, clinical decision support systems, and predictive analytics are rapidly transforming healthcare delivery across diverse global settings. Although these innovations offer substantial potential to improve efficiency, expand coverage, and enhance clinical outcomes, growing concern remains that they may unintentionally reinforce or worsen existing health inequities affecting underserved populations. Because digital and artificial intelligence health research spans multiple disciplines, this narrative review synthesizes evidence from global health, health equity frameworks, artificial intelligence ethics, and sociotechnical systems to examine how digital transformation shapes equitable health outcomes. The findings show that digital and artificial intelligence tools can improve access to healthcare services, reduce geographic and logistical barriers, and enhance diagnostic accuracy. However, these benefits are not evenly distributed. Persistent disparities related to digital connectivity, affordability, digital literacy, data representation, and algorithmic performance may result in exclusion or harm among marginalized groups. To systematically explain these patterns, this review introduces the Equity Pathways Model, which identifies three interconnected mechanisms through which digital health and artificial intelligence influence equity outcomes. The Access pathway explains how digital tools can reduce traditional barriers to care while simultaneously creating new forms of exclusion linked to technology access and digital skills. The Quality of Care pathway highlights how artificial intelligence systems may enhance clinical decision making but also produce biased or unreliable outcomes for underrepresented populations. The System Structural pathway emphasizes how governance arrangements, political economy, institutional capacity, and sociocultural context shape whether digital innovations mitigate or reinforce structural inequities. Together, these pathways provide a framework for distinguishing digital health and artificial intelligence interventions that enhance equity, those that have neutral effects, and those that exacerbate disparities. The review offers an international perspective and proposes equity by design principles to guide researchers, policymakers, and technology developers in ensuring that digital health and artificial intelligence innovations contribute to the advancement of global health equity, particularly in low resource and underserved settings.