搜索结果：Future healthcare journal

The coronavirus disease 2019 (COVID-19) pandemic posed unprecedented global health challenges, necessitating accelerated COVID-19 vaccine development. However, vaccine and booster hesitancy among healthcare workers (HCWs) presented a barrier globally and in South Africa (SA). This study aimed to determine the level of vaccine and booster acceptance among HCWs in public, private and public-private sectors in KwaZulu-Natal (KZN), SA, and to identify the key reasons of vaccine and booster acceptance and refusal. This study was conducted across KZN. A quantitative, descriptive, cross-sectional survey was conducted from October 2024 to April 2025 among doctors, nurses and pharmacists using an online questionnaire. Three hundred and thirty-one HCWs participated in this study with a reliable and standardised questionnaire. Data analysis included descriptive and inferential statistics. Relevant ethics committees and participants provided ethical approval. Average vaccine acceptance rate for vaccine and booster was 92.4% (n = 306) and 73.7% (n = 244), respectively. Public-private recorded the highest acceptance rates for vaccine (96.3%) and booster (85.1%). Primary reasons for vaccine refusal included vaccine-related illness in family (52.4%, n = 11) and rapid development and safety concerns (64%, n = 16), predominantly among private sector. Main booster refusal reasons included safety of multiple booster doses (50%, n = 32) and antibodies from previous COVID-19 infection, mostly in the public sector. Addressing complex determinants of vaccine hesitancy across healthcare sectors necessitates an integrated and sustained approach reinforced by multidisciplinary stakeholder engagement. This study contributes to future pandemic preparedness and vaccination programmes by elucidating reasons influencing vaccine acceptance and refusal among HCWs.

Revascularization remains the cornerstone of treatment for CLTI. Conservative treatment represents an underexposed yet potentially appropriate alternative for selected elderly and frail patients. This population is characterized by high mortality rates, substantial perioperative risks, prolonged hospital stays, increased healthcare costs, and deterioration in functional status and quality of life following invasive interventions. Conservative treatment - including wound care, pain management, and when necessary, minor amputations - may offer an alternative for frail, elderly patients with CLTI. Emerging evidence suggests that, for selected patients, conservative treatment can result in acceptable wound healing rates and survival comparable to invasive strategies, without an increase in major amputation rates. Within the context of appropriate and value-based care, conservative treatment may better balance functional outcomes, healthcare utilization and patient preferences. Future research on prediction models to guide patient selection, cost-effectiveness analyses, and qualitative studies exploring patient-reported outcomes and experiences, should support evidence-based implementation of conservative treatment.

Generative AI (Gen AI), Foundation Models (FMs), and Large Language Models (LLMs) are powerful emerging technologies that demonstrate exceptional capabilities in processing vast amounts of unstructured and structured data, including text, voice, images, video and other formats, and adapting to a wide range of specific tasks. Their immense potential to drive meaningful improvements in treatment outcomes is increasingly evident. The advent of these technologies has marked a transformative era in healthcare, including the fields of radiation oncology and medical physics. Specifically, these powerful technologies offer unprecedented opportunities to analyze domain-specific data, process and synthesize medical images, automate routine tasks, support clinical decision-making, optimize and streamline clinical workflows, and enhance the quality of clinical trials. While these emerging technologies present new opportunities to revolutionize radiation therapy practice, their implementation also raises important educational, ethical, and regulatory considerations. This scoping review highlights benefits, promises, risks, and challenges, such as interpretability, data privacy, regulatory compliance, reproducibility, hallucination, and integration into existing clinical workflow. Finally, emerging opportunities are outlined to guide future research directions. This review paper provides a timely overview of Gen AI, FMs and LLMs, aiming to inform medical physicists, clinicians, and researchers of the evolving role of these disruptive technologies in shaping the future of radiation therapy.

Healthcare artificial intelligence systems often degrade in performance when deployed across institutions, with documented performance drops and perpetuation of discriminatory patterns embedded in data. This brittleness comes, in part, from learning statistical associations rather than causal mechanisms. Causal graph neural networks address this by combining graph-based representations of biomedical data with causal inference to learn invariant mechanisms instead of just spurious correlations. This Perspective reviews the methodology of structural causal models, disentangled causal representation learning, and techniques for interventional prediction and counterfactual reasoning on graphs. We discuss applications across psychiatric diagnosis and brain network analysis, cancer subtyping with multi-omics causal integration, continuous physiological monitoring and drug recommendations. These methods provide building blocks for patient-specific causal digital twins that could support in silico clinical experimentation. Remaining challenges include computational costs that preclude real-time deployment, validation challenges that go beyond standard cross-validation, and the risk of causal-washing where methods adopt causal terminology without rigorous evidentiary support. We propose a tiered framework distinguishing causally inspired architectures from causally validated discoveries and outline future directions, including scalable causal discovery, multimodal data integration and regulatory pathways for these methods. Making practical causal digital twins possible will require an honest assessment of what current methods deliver, sustained collaboration across disciplines and validation standards that match the strength of the causal claims being made.

Pneumococcal conjugate vaccines (PCVs) have transformed the epidemiology of pneumococcal disease while providing a real-world model for understanding how vaccination reshapes antimicrobial resistance (AMR) in Streptococcus pneumoniae. In this Mini Review, we examine PCV-associated AMR dynamics through linked epidemiological, ecological, and genomic processes. We first distinguish two complementary AMR-reducing pathways: direct suppression of vaccine-type serotypes that historically carried antibiotic-nonsusceptible lineages, and indirect reduction of antibiotic exposure by preventing pneumococcal and respiratory disease syndromes that commonly trigger empirical treatment. We then explain why these effects are incomplete. The capsular biosynthesis operon, antimicrobial resistance determinants, and virulence protein genes occupy distinct genomic layers; therefore, post-PCV AMR outcomes depend not only on serotype removal, but also on capsular switching, recombination, mobile resistance elements, lineage fitness, carriage reservoirs, and antibiotic selection. Recent whole-genome sequencing and Global Pneumococcal Sequence Cluster studies show that serotype replacement is best interpreted as lineage-level ecological restructuring, with region-specific consequences for resistant disease. We further discuss how global and regional epidemiology, including serotype-specific invasiveness and uneven vaccine uptake, modifies PCV-associated AMR impact. Finally, we consider higher-valency PCVs, protein-based vaccines, and trained-immunity-based host-directed strategies as complementary future directions. We argue that next-generation pneumococcal vaccines should be evaluated not only by immunogenicity and serotype coverage, but also by their effects on antibiotic use, carriage dynamics, resistant lineage expansion, capsular switching, and long-term population-genomic outcomes.

Nursing education is crucial for preparing students to navigate the complexities of healthcare. The current study aims to investigate the relationships among higher education-related stress, academic self-efficacy (ASE) and satisfaction with clinical education experiences among nursing students to inform current policy and practice in nurse education and support in Saudi Arabia. A descriptive, cross-sectional study was done with undergraduate nursing students at the College of Nursing at Qassim University, in Saudi Arabia. Data were collected by a socio-demographic questionnaire, the student nurse stress index (SNSI) scale, the academic nurse self-efficacy (ANSE) scale and the 'Assessment of Nursing Student Satisfaction with First Clinical Practical Education Questionnaire'. Nursing students exhibited moderate levels of SNSI (M = 70.53, SD = 17.35), ASNE (M = 52.91, SD = 8.96) and satisfaction with clinical education (M = 142.29, SD = 25.09). Furthermore, Interface Worries (includes students' worries and stress when interfacing with educators, clinical personnel, colleagues or the academic environment; 95% CI: -0.407 to -0.120) and Personal Problems (95% CI: -0.205 to -0.019) were statistically significant negative predictors of nursing students' ANSE. The study underscores the significant relationship between higher education-related stress, ASE and satisfaction with clinical education.

Compact, selective gas sensors are crucial for environmental and healthcare monitoring. Using density functional theory + NEGF transport simulations, we investigate the influence of the graphene nanoribbon (GNR) width on the detection of gas molecules such as CO, CO2, and NH3. We show that ultra-narrow GNRs produce molecule-specific transmission fingerprints: CO, CO2, and NH3 adsorption induce distinct shifts and broadenings of resonant channels. When the ribbon width approaches molecular scales, quantum confinement and interference effects strongly enhance the conductance modulation under finite bias. Among the three molecules, NH3 produces the highest sensitivity in the ultra-narrow device (Device 3), particularly at bias voltages above ∼2 V, due to its stronger adsorption energy and pronounced charge redistribution, leading to the largest current suppression relative to the pristine ribbon. These geometry-dependent signatures suggest a practical route to gate-tunable selectivity in GNR-based chemical sensors.

Acute right ventricular failure (ARVF) is a life-threatening condition commonly encountered in the intensive care unit. The treatment of ARVF profoundly changed in the last years, with a growing number of mechanical circulatory support (MCS) devices that have been deployed in clinical practice to support patients with severe forms of ARVF. However, comparative clinical data addressing the superiority of the different MCS strategies are lacking. Several animal models addressing ARVF have been proposed in the literature, and they have been crucial to increase the knowledge on right ventricular (RV) pathophysiology and response to different stressors. Nevertheless, models that reliably mimic acute RV severe failure, ventricular-pulmonary artery uncoupling, and cardiogenic shock are comparatively scarce. Furthermore, only a limited number of experimental studies have incorporated MCS devices in this setting, and direct head-to-head comparisons between different support strategies are largely lacking. This gap in preclinical experiences significantly limits the development of evidence-based algorithms for right-sided MCS deployment. In this review, we summarize currently available animal models of ARVF, critically highlighting their methodological strengths and limitations, and examining the evidence supporting the use of MCS within these frameworks. By highlighting the translational limitations of the existing preclinical experiences, we underscore the urgent need for standardized, reproducible, and clinically relevant ARVF models. Such efforts are essential to improve the current treatment of ARVF, and they could be particularly relevant in developing and optimizing MCS devices and their selection, ultimately enhancing outcomes in patients with ARVF.

In the 2022/2023 training cycle, the VA Prosthetic Orthotic Residency (VAPOR) Program was unable to fill all funded residency positions. Initial debriefing with residency directors revealed a likely cause to be lack of competitiveness of the VAPOR training stipend value, particularly for second-year residents. This demonstrated a need to conduct a training stipend analysis for the VAPOR program. A five-part analysis was conducted including review of the program's stipend history, review of the impact of inflation, and surveys of residency directors and current and former residents. Cumulatively, the analysis revealed that the current stipend was no longer competitive and that the profession was compensating second-year residents more than first-year residents. To effectively compete for top residency candidates, the VA must increase the current training stipend. Ultimately, 4 evidence-based recommendations were derived from the analysis as follows: (1) VAPOR Year 2 (Y2) stipends should be greater than the Year 1 (Y1) stipends; (2) VAPOR Y1 residents should receive a base stipend of $41,620 in addition to a locality adjustment based on the site location; and (3) VAPOR Y2 residents should receive a nonadjusted base stipend valued at 9.3% more than the Y1 stipend or $45,491 to be increased with a locality adjustment based on the site location. A cost-of-living adjustment should be applied to VAPOR program stipends at a minimum of every 3 to 5 years at an increase of 3-5% based on cost-of-living data from other government resources. VA bolstered resident compensation because of these findings.

Despite more than two decades of implementation, there is limited peer-reviewed evidence evaluating whether the multipurpose service (MPS) model effectively meets the contemporary health and aged care needs of rural and regional communities. This gap is particularly significant given recent government inquiries highlighting persistent inequities in rural health service access. The objective of this scoping review was to examine whether the key recommendations of the Royal Commission into Aged Care Quality and Safety and the New South Wales Parliamentary inquiry into Health outcomes for rural, regional and remote New South Wales support whether the multipurpose service model (MPS) is effective as a model of care for service delivery. A scoping review of five databases was undertaken. The first stage included title and abstract review followed by full text reviews. A thematic analysis was undertaken, informed by Braun and Clarke. Nine articles met the criteria for review. Themes that were derived from the data that aligned with the two independent government reports were: MPS model and its infrastructure; funding; staffing and skill mix; and community engagement and needs. Overall, this scoping review established that the MPS model of care is an appropriate model to meet the recommendations of the two government reports. However, there needs to be further investment in staff development to extend scopes of practice when working in rural, regional and remote New South Wales.

This study investigates family physicians' attitudes toward lifestyle medicine, focusing on how these attitudes manifest in their health-promoting lifestyles and the moderating role of their healthcare sector attainment. Employing a quantitative, cross-sectional design, multicentric data were collected from 215 family physicians via an online survey accessed through professional networks. Data were collected using a study-specific demographic and healthcare sector information form, a custom questionnaire designed for attitudes toward lifestyle medicine, and The Health-Promoting Lifestyle Profile II. Findings revealed a positive association between health-promoting lifestyles and attitudes toward lifestyle medicine. The healthcare sector attainment significantly moderated this relationship, with private practice physicians showing a significant link between their health-promoting lifestyles (specifically, personal stress management, health responsibility, physical activity, and nutrition behaviours) and their attitudes toward lifestyle medicine. Conversely, this connection was not statistically significant in the public sector. The findings were discussed within the framework of social and organizational psychology theories and relevant literature. These results suggest that while physicians generally acknowledge lifestyle medicine, its embodiment through personal health behaviours is more pronounced in specific professional contexts. Our study underscores the need for sector-specific strategies to integrate lifestyle medicine more effectively into clinical practice. Policy initiatives should address structural barriers in the public sector that might hinder physicians' personal engagement with health-promoting behaviours, thereby impacting their professional advocacy for lifestyle medicine. Future research should explore the underlying mechanisms within different healthcare sectors and employ longitudinal designs to establish causality, ultimately aiming to enhance physician well-being and advance preventive care across all settings.

Rheumatic heart disease (RHD) is a preventable chronic cardiac condition that causes over 350 000 deaths annually, largely in low and middle-income countries, as a direct result of structural inequalities and inadequate access to comprehensive healthcare. People living with and affected by this disease are a key stakeholder group and need to be directing research priorities. To improve care and provide direction for future research, a group of qualitative researchers and pe living with RHD from six countries convened in Cape Town in 2016. People with RHD shared their lived experiences while RHD researchers, clinicians and advocates presented a spectrum of qualitative research methods to explore these experiences. The Continuum of Care© (CoC, developed by the Medtronic Foundation) was used as a framework to guide participant discussions. Thematic summaries of the discussions were undertaken in an iterative process throughout the workshop. Three themes emerged in the summaries: there is no 'typical' patient journey; a biomedical focus on RHD does not reflect people's lived experiences; and a diversity of research methods is required to investigate experiences of people living with RHD. Qualitative research methods are invaluable for allowing patient 'voices' to be heard. To this end, qualitative approaches should be incorporated in all RHD research to ensure maximum benefit for patients. Greater understanding of the patient journey was gained for strengthening and expanding the global RHD research agenda. Future research should reflect on and incorporate the realities of patients' lived experiences, and these experiences should be integrated into healthcare models for chronic conditions.

Inheritable bleeding disorders (BDs) research has not historically reflected the diversity or needs of the entire community. The National Bleeding Disorders Foundation charged seven multidisciplinary working groups (WGs) with developing a U.S. National Research Blueprint (NRB) for a Bleeding Disorders Research Collaborative (BDRC) inspired by Lived Experience Experts (LEE) and grounded in health equity, diversity, and inclusion (HEDI). The Research and Development and Workforce WGs, in collaboration with the HEDI and LEE WGs, met virtually and in-person to develop recommendations for BDRC operationalization. An agenda of 327 feasible research priorities spanning nine main topics, each with four to six scientific areas of interest is proposed. It captures the hope that new diagnostic and therapeutic technologies and innovative research approaches enriched by LEE and HEDI expertise may advance health equity for all. Key constraints of time, expertise, funding, resources, and diversity were identified as important barriers to the capacitation of the interdisciplinary research workforce required to successfully achieve this research. Mentorship, partnership, training and education, collaboration, and advocacy solutions to these barriers are proposed. The BDRC seeks to capacitate a diverse, inclusive, collaborative workforce, and effectively accelerate research that advances health equity for all. The National Research Blueprint (NRB) was a U.S. National Bleeding Disorders Foundation (NBDF) initiative to better understand all facets impacting research and to set the foundation for what and how inheritable bleeding disorders research should be done in the future. The personal journeys of Lived Experience Experts (LEEs), individuals living with disorders, are highly valuable. It is critical to incorporate them throughout all stages of future research. The NRB made sure LEEs were heard from the very beginning of the development process and throughout. The goal was to place diverse LEEs from across the community at the center of research, with all collaborative partners recognizing LEEs as equal partners.One NRB working group developed a list of priorities to make future inheritable bleeding disorders research more inclusive and reflective of the community. They combined community input and medical, research, and lived experience expertise to choose 327 top research priorities. They ensured all priorities were feasible.Another working group conducted a survey of the current workforce at bleeding disorders centers, often called hemophilia treatment centers (HTC). They asked the different professionals about their interest, capacity, and barriers in doing research. Based on the results, they proposed training and resources needed to develop a diverse workforce that can ensure successful future research.There is a great potential for collaborative research across the country. HTCs can act as hubs in a network of national partnerships. The incorporation of LEEs as valued partners in this Bleeding Disorders Research Collaborative is imperative. Cross-training of LEEs, HTC professionals, and other researchers will be necessary to ensure its success.

Artificial intelligence (AI) is rapidly expanding across the radiation oncology workflow, with applications spanning imaging, contouring, treatment planning, quality assurance, outcome prediction, workflow automation, and clinical decision support. Although technical progress has accelerated substantially, successful clinical translation remains inconsistent. Many of the challenges limiting implementation are not unique to radiation oncology and have previously emerged across healthcare and other high-stakes industries. In this narrative review, we examine radiation oncology AI through the broader lens of cross-industry AI development and deployment. We first summarize the current landscape of AI applications in radiation oncology and then analyze representative examples of successful and unsuccessful AI implementation from healthcare and other sectors. These experiences reveal recurring themes that strongly influence clinical AI success, including data representativeness, robust validation, workflow-centered design, human-AI collaboration, uncertainty management, bias mitigation, operational boundaries, and continuous performance monitoring. We discuss how these lessons apply directly to radiation oncology, where AI systems must function within complex clinical workflows involving imaging, planning, adaptive treatment, quality assurance, and longitudinal patient management. Emerging agentic and multimodal AI systems further amplify both opportunities and risks associated with deployment. Ultimately, the future impact of AI in radiation oncology will likely depend less on isolated algorithmic performance than on the development of trustworthy clinical AI ecosystems. Successful implementation will require rigorous validation, seamless workflow integration, human oversight, regulatory governance, and continuous adaptation. Lessons from healthcare and other industries suggest that the greatest and most durable clinical value may arise from AI systems that augment human expertise, cognitive workflows, and multidisciplinary decision-making rather than replace clinical decision-makers.

Primary health care centres (PHCCs) are the foundation stone of Kuwait's public healthcare system, providing accessible and comprehensive services. Patient satisfaction is a critical indicator of healthcare quality and is associated with improved adherence to medical advice and better health outcomes. Despite international evidence supporting structured family medicine and appointment-based systems, Kuwait's PHCCs continue to operate primarily on a walk-in basis. This study aimed to describe patient satisfaction with a family medicine appointment clinic at the Nassem PHCC in Kuwait. A cross-sectional observational study was conducted between September 2024 and September 2025. The extended recruitment period was necessary to achieve an sufficient sample and capture routine service-utilisation patterns across a full annual cycle. Consecutive patients attending the family medicine appointment clinic were recruited using convenience sampling. A validated, self-administered seven-item questionnaire assessed satisfaction with the doctor-patient relationship using a five-point Likert scale (1&#x202f;=&#x202f;Very Dissatisfied to 5&#x202f;=&#x202f;Very Satisfied). After excluding 11 incomplete records from 264 initial responses, the final analytic sample comprised 253 participants. All analyses were conducted in Jamovi (version 2.5). Principal component analysis confirmed a unidimensional scale structure (KMO&#x202f;=&#x202f;0.949; Bartlett's &#x3c7; 2&#x202f;=&#x202f;2,210, df&#x202f;=&#x202f;21, p&#x202f;<&#x202f;0.001). Internal consistency was excellent (Cronbach's &#x3b1;&#x202f;=&#x202f;0.969). Item means ranged from 4.67 to 4.76, with 74.0-81.0% of respondents selecting the highest satisfaction category across all items, indicating a pronounced ceiling effect. The composite satisfaction score was obviously negatively skewed (skewness&#x202f;=&#x202f;-3.59, kurtosis&#x202f;=&#x202f;18.0). A statistically significant age-group difference in satisfaction was observed (Kruskal-Wallis &#x3c7; 2&#x202f;=&#x202f;24.60, df&#x202f;=&#x202f;4, p&#x202f;<&#x202f;0.001, &#x3b5; 2&#x202f;=&#x202f;0.098); no significant differences were found by nationality or gender. Patients attending the Nassem PHCC family medicine appointment clinic reported high Overall Satisfaction. A significant age-related variation was observed, with older patients reporting higher satisfaction than younger adults. Ceiling effects, cross-sectional design, convenience sampling, and potential social desirability bias limit the conclusions that can be drawn. Future research should employ longitudinal designs, comparator groups, and more sensitive instruments.

There is limited knowledge about how patients experience artificial intelligence (AI) in healthcare. Concerns regarding safety and quality could be a barrier between patients and the healthcare system when implementing AI. It is crucial to understand patients' attitudes, and how they may react to AI being implemented when aiming to preserve their trust. We investigated patients' attitudes toward AI-assisted colonoscopy to gain insights that can be used when introducing this new technology to future patients. This is a qualitative study based on semi-structured focus group interviews. Habile, Danish-speaking adults who had received an AI-assisted colonoscopy were invited. We conducted six focus group interviews, with 20 participants in total. All interviews were recorded and transcribed. Data were analysed using thematic analysis. Patients described a sense of vulnerability in relation to undergoing a colonoscopy. Three main themes were identified. The first theme is 'Empathy and professionalism-trust in AI-assistance in colonoscopy depends on human factors'. The second and the third themes are 'Information about AI should be proportional to the consequences', and 'Trying to make sense of AI during colonoscopy-balancing curiosity, irrelevance, and vulnerability'. Trust in AI-assistance is dependent on trust in the clinicians. While AI has many strengths, it cannot provide empathy, which is essential for patients in distressing moments, such as during a colonoscopy. AI should be considered a supportive tool, while the endoscopist remains responsible for clinical decisions and patient outcomes. If written information about AI is provided, it should be informative but concise, avoiding details that generate more confusion than clarity.

Amputation is a critical event leading to the permanent loss of limb function and long-term functional impairment, posing a substantial burden on global healthcare systems. However, the global burden, demographic patterns and future trajectory of amputation-related disability have not been comprehensively quantified. We analysed Global Burden of Disease (GBD) 2021 data to assess the prevalence and years lived with disability (YLDs) for seven amputation subtypes (fingers, thumbs, toes, unilateral and bilateral upper and lower limbs) from 1990 to 2021. Projections to 2050 were made using the Bayesian age-period-cohort (BAPC) modelling. In 2021, the global total number of amputation cases reached approximately 445 million (95% uncertainty intervals [UI]: 409-486 million), with an age-standardized rate (ASR) of 5330 (95% UI: 4890-5820) cases per 100&#x2009;000 population. From 1990 to 2021, the overall global burden of amputation demonstrated a declining trend in ASR, although the absolute number of cases increased substantially. Among all amputation subtypes, finger amputation (AMP-F) had the highest prevalence, accounting for 50.3% of all cases, followed by thumb amputation (AMP-Th) at 22.4% and toe amputation (AMP-Toe) at 19.0%; these three subtypes alone accounted for over 91% of all cases. Regarding YLDs, unilateral lower limb amputation (AMP-LL1) contributed most significantly (20.8%), followed by bilateral upper limb amputation (AMP-UL2) at 18.9%, despite their relatively lower prevalence. In terms of the YLD burden, the age distribution exhibited a spindle-shaped pattern, peaking in the 30-59 age group overall, with notable subtype-specific variations (e.g., AMP-LL1 peaking at &#x2265;&#x2009;95&#x2009;years and bilateral lower limb amputation [AMP-LL2] prominent in middle-aged groups, particularly 50-64&#x2009;years in certain regions). Additionally, the etiological profile shifted markedly, with conflict and terrorism rising sharply from the 8th to the 4th leading driver of amputation burden globally, exerting a particularly strong impact in North Africa and the Middle East. Projections indicate substantial future increases in both prevalence and YLDs associated with AMP-LL1, necessitating heightened vigilance. Although the age-standardized prevalence of amputation slightly declined globally from 1990 to 2021, the absolute number of cases increased significantly, exhibiting pronounced demographic and geographic disparities. To mitigate this escalating burden, tailored strategies integrating prevention and long-term rehabilitation planning are urgently needed.

Advances in data science and medical artificial intelligence (AI) raise complex philosophical and ethical quandaries about what it means to know a person or a community through data and what kinds of people and societies we are becoming in this era of predictive data science. Drawing on four lightly fictional but reality-informed case studies in mental health, radiology, genomics and environmental public health, we reflect on how AI technologies, largely built on Western biomedical traditions, may conflict with relational, spiritual and Indigenous understandings of health and wellbeing. This may manifest as epistemic friction, algorithmic fatalism and diminished trust in patient-clinician relationships. Besides familiar concerns regarding bias and transparency, this paper advances the discourse on the ethics of medical AI and data science in healthcare by shifting the analysis from epistemology (how AI systems know, classify and predict) to ontology (the study of the nature of being, as reconfigured by data and AI). We argue that AI systems may inflict significant ontological harm by reconfiguring identity, moral agency and imagined futures and advocate for a renewed medical humanism driven by inter- philosophies dialogue, cross-cultural ethics and ecocentric approaches to care. From Bamenda, Cameroon, to Mthatha, South Africa and Toronto, Canada, the future of medical AI must be defined by the moral and philosophical traditions that people already live by. African, Indigenous, Islamic, Buddhist, Confucian and marginalised Western worldviews should not be treated as peripheral critiques, but as constitutive resources for building inclusive, human-centred health technologies. We conclude that bioethics should be recognised as a core infrastructure in global health, on equal footing with data science, medicine, biomedical research and health innovation.

Oral and periodontal diseases are among the most prevalent chronic disorders worldwide and arise from complex interactions between microbial biofilms and dysregulated host immune responses. Despite significant advances in conventional therapies, clinical management remains challenging because of limited drug penetration, rapid clearance within the oral cavity, and poor patient compliance. In this context, microneedle (MN)-based drug delivery systems have emerged as promising minimally invasive platforms for localized and controlled therapeutic delivery. However, the adaptation of MN technologies from transdermal to oral applications requires application-specific redesign owing to the unique anatomical, physiological, and mechanical characteristics of oral tissues. Current literature also remains fragmented, particularly regarding the integration of oral tissue biology, advanced manufacturing strategies, and multifunctional MN design. This review provides a comprehensive and critical analysis of MN systems for oral and periodontal applications, with particular focus on the enabling role of additive manufacturing (AM). First, the biological characteristics of oral tissues and their implications for drug delivery are discussed, followed by an overview of MN technologies, biomaterials, and fabrication approaches. Particular emphasis is placed on oral application-specific design considerations, including mechanical constraints, penetration depth, bioadhesion, retention, and controlled drug release. Emerging therapeutic applications, ranging from antibacterial and anti-inflammatory therapies to immunomodulatory and regenerative strategies, are also critically evaluated. Recent advances in 3D-printed MNs are highlighted, emphasizing their potential for customizable architectures, integrated drug delivery systems, and patient-specific therapeutic platforms. In parallel, major translational challenges, including mechanical reliability, retention under salivary conditions, regulatory complexity, and manufacturing scalability, are critically discussed. Future perspectives involving the integration of artificial intelligence (AI), smart biomaterials, biosensor technologies, and precision medicine approaches are also explored. Beyond summarizing recent advances, this review identifies the key scientific challenges, current knowledge gaps, and emerging engineering strategies required for the successful clinical translation of 3D-printed oral and periodontal MN systems. Overall, it provides a critical roadmap for the rational design and development of next-generation personalized MN platforms, highlighting the convergence of advanced biomaterials, biofabrication technologies, and precision medicine as a foundation for future oral healthcare.