PLOS Digital Health

Computer vision models for chest X-ray interpretation hold significant promise for global healthcare, but their clinical value depends on equitable development across diverse populations. We conducted a scientometric analysis to examine authorship patterns, geographic distribution, and dataset origins to assess potential disparities that could affect clinical applicability. We systematically reviewed literature on computer vision applications for chest X-rays published between 2017-2025 across multiple databases, including PubMed, Embase and SciELO databases. Using Dimensions API and manual extraction, we analyzed 928 eligible studies, examining first and senior author affiliations, institutional contributions, dataset provenance, and collaboration patterns across different income classifications based on World Bank categories. High-income countries dominated research leadership, representing 55.6% of first authors and 59.7% of senior authors; no first authors were affiliated with low-income countries. China (16.93%) and the United States (16.72%) led in first authorship positions. Most datasets (73.6%) originated from high-income settings, with the United States being the largest contributor (40.45%). Private datasets were most frequently used (20.52%). Cross-income collaborations were rare, with only 3.9% of publications involving partnerships between high-income and lower-middle-income countries. Findings reveal substantial disparities in who shapes computer vision research on chest X-rays and which populations are represented in training data. These imbalances risk developing AI systems that perform inconsistently across diverse healthcare settings, potentially exacerbating healthcare inequities. Addressing these disparities requires coordinated efforts to develop globally representative datasets, establish equitable international collaborations, and implement policies that promote inclusive research practices.

Abstract Background: Cardiovascular disease is the leading cause of mortality in Nigeria and across sub-Saharan Africa, with rising incidence attributable to urbanisation, sedentary lifestyles, and limited access to early detection tools. Concurrently, patient dropout from rehabilitation programs remains a critical operational challenge for Nigerian clinics, with many patients failing to return after their initial consultation. Methods: We developed CardioAI, an Explainable Artificial Intelligence system comprising two predictive modules. The cardiovascular risk module trained four machine learning models - Logistic Regression, Random Forest, Gradient Boosting (XGBoost), and a Multilayer Perceptron - on a combined UCI Heart Disease dataset of 1,025 patient records. A novel Lifestyle Risk Index was engineered from five modifiable clinical markers. SHAP (SHapley Additive exPlanations) was applied for per-prediction feature attribution. The patient retention module trained three classifiers on a synthetic dataset of 800 records, modelling 10 operational and behavioural dropout factors. An NLP and OCR pipeline using Tesseract v5.5 and spaCy was implemented for clinical document processing. Results: The cardiovascular risk module achieved an AUC-ROC of 0.999 (XGBoost), 0.998 (Random Forest), 0.994 (MLP), and 0.927 (Logistic Regression) on the held-out test set. Cross-validated AUC with constrained tree depth was 0.97, confirming generalisation. SHAP analysis identified the Lifestyle Risk Index, ST depression, resting blood pressure, exercise-induced angina, and cholesterol as the five most influential predictors. The retention module achieved AUC-ROC of 0.66 (Logistic Regression), demonstrating the difficulty of dropout prediction with synthetic data. Conclusions: CardioAI demonstrates that explainable machine learning can provide clinically actionable cardiovascular risk assessment and patient retention intelligence in a low-resource Nigerian healthcare context. The system is freely deployable, open-source, and designed for pilot validation in teaching hospitals across Lagos and Port Harcourt. Keywords: cardiovascular risk prediction, machine learning, explainable AI, SHAP, patient retention, clinical decision support, Nigeria, sub-Saharan Africa, XGBoost, random forest, digital health

Artificial intelligence (AI) is increasingly positioned as a transformative tool in education and health. Yet empirical evidence on AI readiness in low-and middle-income countries, particularly among youth, remains scarce. This study examined patterns of adoption, equity determinants, and ethical awareness among Ghanaian youth to inform responsible AI integration in education and health systems. A cross-sectional survey was conducted among 200 youth aged 18-35 years in Ghana. Descriptive statistics, chi-square tests, and logistic-regression analyses were used to assess AI adoption, equity patterns, and predictors of readiness. Most participants reported current (89%) or prior (65%) use of AI tools. Accessibility was a significant positive predictor of adoption ({beta} = 0.142, p = 0.001), whereas limited internet connectivity ({beta} = -0.088, p = 0.049) and perceived exclusion or inequity ({beta} = -0.109, p = 0.026) were significant negative predictors. Gender and age differences indicated persistent digital inequities. Ethical concerns were widespread: 51% were somewhat concerned and 39% very concerned about data privacy, algorithmic bias, and transparency. Ghanaian youth exhibit high AI readiness, but it is distributed in structurally uneven and ethically contested contexts. Readiness is best understood as a dynamic interaction between technical access, social inclusion, and trust. Translating readiness into equitable implementation will require investments in digital infrastructure, ethical governance, and participatory design. This study provides one of the first quantitative assessments of AI readiness among African youth and offers an evidence base for developing trustworthy, inclusive AI applications, such as healthcare and educational chatbots, that are grounded in local realities. Author SummaryArtificial intelligence (AI) is often presented as a solution to challenges in healthcare and education. However, there remains limited evidence on peoples readiness to use AI in low-and middle-income countries and on the ways in which equity and ethics shape that readiness. We surveyed 200 youth in Ghana to understand their use of AI tools, perceptions of fairness and ethical concerns. Most participants were already using AI, yet adoption was uneven. Access to reliable internet and devices strongly increased use, while perceptions of exclusion and limited connectivity reduced it. Many youths expressed concern about data privacy, bias, and transparency in AI systems. These findings show that Ghanaian youth are eager but cautious adopters who value fairness and accountability. Building equitable and trustworthy AI systems in education and health will require improving access, addressing social inequalities, and involving youth directly in the design and governance of new technologies.

BackgroundDigital health dashboards can enhance health system performance by transforming routinely collected data into actionable insights for decision-making. In low-resource settings, however, their effectiveness depends not only on the relevance of indicators but also on system reliability within constrained digital infrastructure. Neonatal mortality remains a major global health challenge, with the highest burden in low- and middle-income countries, where many deaths are preventable through timely, evidence-based interventions. Continuous monitoring of care processes and outcomes is therefore essential. To support this need, we developed the NEST360 Implementation Tracker (NEST-IT) using R Shiny to support quality improvement across more than 100 hospitals in sub-Saharan Africa. As the platform scaled to over half a million records and increasing concurrent users, performance constraints emerged, particularly in hospitals with limited computing resources, threatening timely access to critical information. ObjectiveThis study aimed to describe optimization strategies applied to the NEST-IT dashboard and evaluate their impact before and after implementation. MethodsA structured optimization process was implemented following established R Shiny performance principles. Dashboard profiling was first conducted to identify key bottlenecks, after which targeted improvements were applied to improve efficiency and responsiveness. A quasi-experimental pre-post evaluation (December 2023-August 2024) assessed performance using three indicators: server processing time, visualization rendering time (VRT), and Time to First Byte (TTFB). Metrics were measured repeatedly during one-month baseline and post-optimization periods and summarized using mean values. ResultsFour primary bottlenecks were identified: delayed server responses, slow visualization rendering, inefficient data handling, and inconsistent device performance. Following optimization, interactive plot load time decreased from 10.1 to 2.7 {+/-} 0.6 seconds (73.3% improvement). Visualization rendering improved from 3.61 to 1.62 seconds, while server processing time fell from 2.3 {+/-} 0.7 to 0.8 {+/-} 0.3 seconds. TTFB improved from 1.9 {+/-} 0.4 to 0.6 {+/-} 0.2 seconds, and system uptime increased from 92.5% to 99.2%. ConclusionPerformance optimization substantially improved dashboard responsiveness, enabling timely access to critical neonatal information in resource-constrained hospital settings. The findings provide practical, evidence-based framework for improving the performance of R Shiny dashboards and demonstrate scalable strategies for delivering reliable digital decision-support tools in low-resource health systems.

Introduction: Timely, protocol-adherent clinical decisions are crucial for reducing neonatal mortality in low-resource settings. Translating extensive national guidelines into bedside practice remains challenging. Objective: We developed and evaluated AIFYA, a human-supervised, large language model LLM based clinical decision support system CDSS aligned with Kenya's national newborn care protocols. Methods: This prospective mixed methods early stage evaluation guided by the DECIDE-AI framework embedded AIFYA into routine workflows at two public health facilities Level 5 and Level 4 in Bungoma County Kenya from September 2024 to June 2025. Primary outcomes were adoption measured by cumulative neonatal cases managed training reach assessed by credentialed healthcare workers HCWs and guideline and citation concordance evaluated through blinded review of 118 AI generated recommendations by two neonatologists with adjudication by a third. Secondary outcomes included protocol adherence and triage to decision time. Results: A total of 50 HCWs were trained and 550 neonatal cases were managed over 10 months. Among surveyed HCWs n equals 33, 76 percent were female with mean age 32.1 years. Expert review found 75 percent of recommendations were correct and 15 percent partially correct with strong inter rater reliability weighted Cohen's kappa 0.85 and 95 percent CI 0.79 to 0.91. Citation accuracy was 96 percent. In 40 complex dosing scenarios 75 percent of outputs were rated correct. The median triage to decision time was 23 minutes with interquartile range 18 to 31. Implementation was supported by an offline first architecture and a facility based coaching model sustaining engagement despite staff turnover. Conclusion: A human supervised AI CDSS directly and transparently anchored to national clinical guidelines can be successfully implemented in routine low resource neonatal care settings. The system demonstrated high user adoption and strong expert rated concordance. High citation accuracy builds clinical trust ensuring safety and enabling auditable AI. These findings support progression to controlled multi site trials to evaluate clinical effectiveness. Keywords: Neonatal care Clinical decision support system Large language model Artificial intelligence Human supervised Low resource settings Guideline adherence Digital health Kenya

ObjectiveTo evaluate whether new tuberculosis (TB) tools in the electronic health record (EHR) can support latent TB infection (LTBI) screening, testing and treatment among children and adolescents in a primary care setting. Study DesignThis retrospective cohort study included children and adolescents between the ages 1-25 years who had a well-child or well-adolescent visit at a Federally Qualified Health Center in Oakland, California, from December 2021 to December 2022. Four new EHR tools were introduced for the completion and documentation of TB risk factor screening, testing and treatment. Data were extracted from the EHR to identify gaps in these steps, and logistic regression was used to examine factors associated with completion of TB infection screening and testing. Acceptability was evaluated using provider satisfaction surveys before and after the implementation of TB EHR tools. ResultsOf 5,879 individuals (median age of 9 years at first visit, interquartile range (IQR) 4-13 years), 94% completed TB risk factor screening. Among those with a new risk factor, 59% had a TB infection test ordered and 96% completed testing. Ten participants (3%) tested positive, all initiated LTBI treatment, and most (n=7, 70%) completed treatment. Overall, 5,162 (88%) individuals completed their LTBI care cascade. Younger children ages 1-4 years were more likely to be screened for TB risk factors, but were less likely to be tested. Provider satisfaction increased from 40% to 71% for risk factor screening, and 36% to 77% for test ordering. ConclusionEHR tools supported completion of the pediatric LTBI care cascade, while also increasing provider satisfaction. EHR-based solutions show promise as part of multi-component strategies to address gaps in LTBI care for children and adolescents.

Background: Digital health tools are increasingly promoted for strengthening health information systems in low- and middle-income countries, yet routine maternal and child health (MCH) data in tribal primary health centres (PHCs) in India remains underutilised for local decision-making. Top-down digital tools often fail in low-resource settings because they are designed without meaningful input from end-users. Co-creation approaches for digital health in tribal and indigenous settings are largely unexplored. Methods: We conducted an action research study in three tribal PHCs under the Integrated Tribal Development Agency (ITDA), Rampachodavaram, Andhra Pradesh, India. We applied the Three Co's Framework (Co-Define, Co-Design, Co-Refine) to co-create data science solutions for MCH decision-making with five medical officers, 24 auxiliary nurse midwives, and 36 accredited social health activists across two action research cycles (August 2023 to August 2024). Co-creation involved collaborative indicator definition, data modelling, data quality validation, health facility catchment area construction, spatial analysis, and interactive dashboard development. Keller's Data Science Framework was employed using R to structure the analytical pipeline, and Data.org's Data Maturity Assessment (DMA) was used to assess organisational data maturity pre- and post-intervention. Findings: During Co-Define, co-creators identified a fundamental mismatch between system outputs (aggregate statistics for upward reporting) and their operational need for individual-level, geographically disaggregated, prospective information. Co-Design produced five interconnected data science solutions: (1) 42 co-defined MCH indicators grounded in clinical workflows; (2) a data model linking individuals, health services, providers, and facilities; (3) a data quality framework using the pointblank R package; (4) health facility catchment area boundaries constructed from scratch using medical officers' local knowledge, enabling spatial analysis that revealed significant clustering of ANC coverage and anaemia prevalence; and (5) an R Shiny dashboard integrating these solutions into an offline-capable interface with lifecycle-organised views and village-level navigation. The DMA showed moderate improvement in organisational data maturity from 5.04 to 5.75 out of 10, with the largest gain in Analysis (+1.90). Co-Refine continued beyond the formal study period, with two transferred medical officers maintaining analytical engagement from new postings. Interpretation: The Three Co's Framework, combined with a data science approach, provided a structured yet flexible method for co-creating locally relevant data science solutions in a tribal setting. The framework's explicit separation of problem definition from solution design was particularly valuable in a context where "the problem" is typically defined externally. Co-creation in tribal digital health settings is feasible and produces solutions that address locally articulated needs.

Rare diseases affect millions worldwide and are associated with long diagnostic delays, limited access to treatments, and substantial challenges in daily care and coordination. Digital health technologies, including mobile apps, telehealth, and data-sharing platforms, offer opportunities to improve care and quality of life for people living with rare diseases. As these tools rapidly expand, this study examines the needs, expectations, and conditions for successful adoption of patient-centered digital solutions among individuals living with rare diseases and their families. Using a mixed-methods design, we surveyed 149 patients and caregivers, and conducted follow-up focus groups with 15 participants. Our findings highlight the essential role of digital tools in supporting people with rare diseases and their families. Key priorities include centralized health data, support for patient-generated data, and improved communication and information exchange with clinicians. Participants strongly emphasized the value of telehealth to reduce travel and simplify daily life, as well as patient-centered tools for diagnosis and emergency situations. Future digital solutions should integrate system-wide data, incorporate AI, and provide support during stressful situations, ultimately reducing patient burden despite persistent structural challenges. Respondents expressed strong interest in technologies that place patients at the center of care and improve coordination across providers. Overall, our study identifies actionable targets for innovation and highlights technological, regulatory, and resource-related barriers that must be addressed to advance patient-centered digital solutions for rare diseases and guide future research and policy development. Author SummaryPeople living with rare diseases often wait years for a diagnosis and struggle with complex, fragmented care. Digital health technologies could help address these challenges, but only if they are designed around patients real needs. To better understand these needs, we surveyed 149 patients and caregivers in Quebec and held follow-up discussions with 15 participants. They emphasized the importance of centralized access to health information, better communication with clinicians, and tools that support patient-generated data. Telehealth was especially valued because it reduces travel and simplifies everyday life. Our findings show that people with rare diseases want digital solutions that reduce their daily burden, improve coordination across providers, and support them during stressful moments such as emergency visits or the diagnostic process. This work provides practical guidance for designing patient-centered digital tools and highlights system-level barriers that must be addressed to ensure these innovations truly benefit the rare disease community.

Irritable Bowel Syndrome (IBS) affects a substantial proportion of university students, yet its factors remain incompletely characterised in South Asian populations. We reanalysed a publicly available dataset of 550 Bangladeshi students from Hasan et al. (2025), conducting a data audit that identified implausible records, including males reporting menstrual symptoms, and reduced the analytic sample to 506 observations. Using Explainable Boosting Machines (EBMs), which capture non-linear effects and pairwise interactions without sacrificing interpretability, we found that psychological distress, elevated BMI and academic dissatisfaction were the strongest predictors of IBS (mean AUC = 0.852 across 100 stratified train-test splits). Critically, several findings diverged from the original logistic regression analysis. Physical activity showed a non-linear risk pattern only at high intensity, the association with gender was substantially weaker when we accounted for metabolic and psychological factors as well and malnourishment does not have a strong an impact as in the original study. These divergences likely arise because the machine-learning model captures non-linear effects and interactions that were not represented in the original regression specification. Our findings underscore the value of reanalysing existing datasets with methods suited to capturing complexity and highlight data quality verification as a necessary step in the secondary analysis.

Despite robust policy frameworks, Ugandas digital health landscape is characterised by fragmentation--often termed "Pilotitis"--where stand-alone applications impede the integrated delivery of health, nutrition, and food security services. As part of the IGNITE project, this study mapped existing digital health systems (DHSs), identified systemic gaps, and explored opportunities and resource requirements for sustainable integration of existing Health, Nutrition and Food security data systems. The IGNITE project adopted a mixed-methods design; however, this paper reports findings from the first phase--a national cross-sectional survey conducted in Uganda. The survey mapped digital health, nutrition, and food security systems, identifying gaps, resource needs, and potential actions. Stakeholders from government, NGOs, academia, UN agencies, and frontline health workers were included using purposive and snowball sampling. Data were collected online and through field support. Of 134 respondents, 110 with [≥]70% survey completion was included in the analysis. While 93% of respondents utilise digital tools (predominantly DHIS2 and mobile apps), only 20% reported full automated integration with national platforms. Critical barriers to interoperability included a lack of technical expertise (90%), insufficient DHIS2 training (82%), different data formats (77%), and infrastructure constraints (75%). Respondents identified workforce development (56%) and DHIS2 use and adoption (29%) as primary opportunities. Immediate priorities include staff training and provision of mobile hardware, while long-term strategies focus on standardised data formats (78%) and formalised governance frameworks for Integrated platforms (64%) and automated data exchange (56%). Uganda possesses a vibrant but disconnected digital ecosystem. Transitioning from isolated "data islands" to a cohesive system requires addressing the massive technical capacity gap and establishing mandated interoperability guidelines. The findings provide a data-driven roadmap for the Ministry of Health and partners to optimise digital health adoption, ensuring that nutrition and food security interventions are supported by a unified, evidence-informed digital architecture

Causal discovery algorithms are often leveraged for inferring causal relationships and recovering a causal model from data. However, causal discovery from data alone is limited by the structural constraints of the used dataset, the lack of causal logic, and the lack of external knowledge. Thus, data-driven causal discovery can only suggest possible causal relationships at best. To overcome these limitations, Large Language Models (LLMs) and knowledge systems, such as Retrieval-Augmented Generation (RAG), have been proposed as alternatives to data-driven causal discovery and as a method to augment causal discovery algorithms. Using an expert-defined causal graph of chronic lower back pain, we further propose knowledge graph based RAG systems, such as GraphRAG, as an improvement over RAG systems for augmenting causal discovery (F1 0.745), benchmarking its performance against augmenting causal discovery with an LLM (F1 0.636), augmenting causal discovery with RAG (F1 0.714), and causal discovery alone (F1 0.396). We also explore the impact of different prompting methods for causality, such as querying for the plausibility of causal relationships, the presence of statistical associations, and the existence of temporal causal relationships, as inspired by the methodology of the domain experts constructing our ground truth. Lastly, we discuss how applications of LLMs, RAG, and graph-based RAG systems can impact and accelerate the causal modeling of chronic lower back pain by bridging the gap between domain knowledge and data driven approaches to causal modeling. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/26346255v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@f3387org.highwire.dtl.DTLVardef@2dforg.highwire.dtl.DTLVardef@bc839aorg.highwire.dtl.DTLVardef@63f6ea_HPS_FORMAT_FIGEXP M_FIG C_FIG

Introduction Large language models are increasingly being used in healthcare. In interventional pain medicine, clinical reasoning is essential for procedural planning. Prior studies show that simplified prompts reduce clinical detail in AI-generated responses. It remains unclear whether this reflects knowledge loss or simply prompt-driven suppression of information. Methods We performed a controlled comparative study using 15 standardized low back pain questions representing common interventional pain questions. Each question was submitted to ChatGPT under three conditions, professional-level prompt (DP), fourth-grade reading-level prompt (D4), and clinician-directed rewriting of the D4 response to a medical level (U4[->]MD). No follow-up prompting was allowed. Three physicians independently rated responses for accuracy using a 0-2 ordinal scale. Clinical completeness was determined by consensus. Word count and Flesch-Kincaid Grade Level (FKGL) were also measured. Paired t-tests compared conditions. Results Accuracy was highest with professional prompting (1.76). Accuracy declined with the fourth-grade prompt (1.33; p = 0.00086). When simplified responses were rewritten for clinicians, accuracy returned to baseline (1.76; p {approx} 1.00 vs DP). Clinical completeness followed the same pattern showing DP 80.0%, D4 6.7%, U4[->]MD 73.3%. Fourth-grade responses were shorter and less complex. Upscaled responses were more complex and similar in length to professional responses. Inter-rater reliability was low (Fleiss {kappa} = 0.17), but trends were consistent across conditions. Conclusions Reduced clinical detail under simplified prompts appears to reflect constrained output rather than loss of knowledge. Clinician-directed reframing restores omitted content. LLM performance in interventional pain depends strongly on prompt design and intended audience.

Diabetic retinopathy (DR) is a leading cause of vision impairment, requiring accurate and scalable diagnostic tools. Foundation models are increasingly applied to clinical imaging, but concerns remain about their calibration. We evaluated DINOv3, RETFound, and VisionFM for DR classification using different transfer learning strategies in BRSET (n = 16,266) and mBRSET (n = 5,164). Models achieved high discrimination in binary classification (normal vs retinopathy) in BRSET (AUROC 0.90-0.98), with DINOv3 achieving the best under full fine-tuning (AUROC 0.98 [95% CI: 0.97-0.99]). External validation on mBRSET showed decreased performance for all models regardless of the fine-tuning strategy (AUROC 0.70-0.85), though fine-tuning improved performance. Foundation models achieved strong discrimination but poor calibration, generally overestimating DR risk. While the generalist model, DINOv3, benefited from deeper fine-tuning, miscalibration remained evident. These findings underscore the need to improve calibration and the comprehensive evaluation of foundation models, which are essential in clinical settings. Author summaryArtificial intelligence is increasingly being used to detect eye diseases such as diabetic retinopathy from retinal images. Recent advances have introduced "foundation models," which are trained on large datasets and can be adapted to new tasks. We aimed to evaluate how well these models perform in a clinical prediction context, with a focus not only on accuracy but also on how reliably they estimate disease risk. In this study, we compared different types of foundation models using two independent datasets from Brazil. We found that while these models were generally good at distinguishing between healthy and diseased eyes, their predicted risks were often poorly calibrated. In other words, the estimated probabilities did not consistently reflect the true likelihood of disease. We also examined whether adapting the models to the target population could improve performance. Although this approach led to improvements, calibration issues remained. However, post-training correction improved the agreement between predicted risks and observed outcomes. Our findings highlight an important gap between model performance and clinical usefulness. We suggest that improving the reliability of risk estimates is essential before such systems can be safely used in healthcare.

BackgroundPersistent inequities in immunisation coverage, particularly among zero-dose and under-immunised children, continue to challenge Pakistans Expanded Programme on Immunisation. Weak feedback loop, inconsistent data quality, and limited real-time monitoring impede effective decision-making. This Implementation Research was conducted under the MAINSTREAM Initiative funded by Alliance for Health Policy and Systems Research (AHPSR) and supported by the Aga Khan Community Health Services Department and National Institutes of Health Pakistan to design, implement, and evaluate a digital monitoring and action planning tool to strengthen data-driven decision-making within routine immunisation systems. Methodology/Principal FindingsA co-creation approach was employed to design a digital monitoring solution through inclusive consultations, key informant interviews, and focus group discussions with EPI Punjab at provincial and district levels. The solution included a customized mobile application for data collection and a Power BI visualization dashboard to map low-coverage areas, identify drivers of dropouts and zero-dose children, and capture caregivers information sources to inform targeted communication. The intervention was piloted in 60 households across six clusters of a Union Council of District Lahore. Advanced analytics identified reasons for non-vaccination and missed opportunities, generating tailored recommendations and practical plans for program managers. The analysis assessed acceptability, adoption, fidelity, and perceived scalability through field observations, system use, and stakeholder feedback. The co-developed digital tool enhanced visibility of coverage gaps through UC-level mapping, real-time dashboards, and structured action planning. Pilot testing in Lahore showed strong acceptability, ease of use, fidelity, and adaptability among managers, supervisors, and vaccinators. Scalability and sustainability potential were demonstrated, though barriers included leadership turnover, system fragmentation, workload pressures, and resource constraints. ConclusionThe tool demonstrated feasibility to strengthen immunisation equity, accountability, and responsiveness. Co-creation with stakeholders enhanced ownership, operational relevance, and adoption, while complementing existing platforms. Sustainability will depend on effective integration, local ownership, capacity building, and accountability, while scalability requires interoperability, resource commitment, policy support, and alignment with existing workflows. Author SummaryMany children in Pakistan still miss routine vaccinations, especially those who have never received any vaccines or who drop out before completing the schedule. These children are often invisible within routine reporting systems, making it difficult for health managers to identify gaps and respond effectively. In this study, we developed and evaluated a digital monitoring and action planning tool designed to help immunisation managers and frontline workers better identify and respond to these gaps. We worked collaboratively with provincial and district immunisation staff to co-design a user-friendly system that combines mobile data entry with interactive dashboards for supervision and planning. The tool was piloted in an urban district of Lahore, where health workers and managers reported high acceptability and ease of use. The system helped improve visibility of missed children, supported follow-up actions, and strengthened accountability across different levels of the immunisation program. Our findings show that digitally enabled, co-created tools can strengthen routine immunisation systems by improving data use for action, supporting more responsive service delivery, and promoting equity. This work offers practical insights for scaling digital solutions to reach underserved populations and improve immunisation performance in similar low- and middle-income settings.

Loneliness and psychological well-being are increasingly recognized as critical public health concerns, yet their multi factorial determinants remain poorly understood. Traditional research often examines demographic, lifestyle, or social variables in isolation, yielding fragmented insights that overlook complex psychosocial interactions. In this study, we leverage a rich behavioral and psychological dataset from the Human Phenotype Project (HPP) to examine how lifestyle behaviors, social health indicators, and demographic characteristics collectively influence mental health outcomes. Employing advanced machine learning (ML) methods, including feature engineered representations, classical predictive models, and Large Language Model (LLM) classifiers, we identify latent psychosocial patterns associated with loneliness and psychological symptoms. Our approach combines predictive performance with interpretability, enabling the identification of key drivers of well-being across heterogeneous populations. Results indicate that certain lifestyle and social engagement factors consistently correlate with lower loneliness and improved psychological health, while other influences are context-dependent. This work demonstrates the potential of integrating computational modeling with psychological theory to reveal complex, multidimensional determinants of mental health, offering insights for targeted interventions, digital health applications, and evidence-based public health strategies.

BackgroundDigital health plays an indispensable role in facilitating data analysis and use for enhancing healthcare delivery across health settings. However, there is scant information on the extent to which digital health influences the improvement of primary health services delivery through data use. This study examined the determinants that influence the use of digital health to improve health service delivery in council hospitals in Tanzania. MethodsA cross-sectional design was employed in six regions, involving 12 council hospitals. We used a self-administered questionnaire to collect data from 203 members of hospital quality improvement teams. Descriptive analysis was used to determine the frequency, proportion, and mean of responses, while bootstrapping analysis was conducted to test the statistically significant influence of digital health factors on data use for improving health service delivery. ResultsResults show moderate agreement on data compatibility for planning and decision-making, with 40.4% of respondents agreeing it supports ordering commodities, 43.8% for staff allocation, and 38.4% for planning. However, dissatisfaction was higher for user-friendliness (47.8%), reliability (up to 65.5%), and usefulness (up to 63.5%). Overall, 50.2% (M=2.74{+/-}0.87) disagreed that digital systems effectively support data use. Structural model analysis confirmed significant positive influence of usefulness ({beta}=0.199, p<0.001) and access to quality data ({beta}=0.729, p<0.001) on data use, which strongly impacted service delivery ({beta}=0.593, p<0.001), despite some factors showing no direct influence. ConclusionThe study finds that current digital health initiatives only modestly improve the user-friendliness, reliability, and usefulness of data systems, partly due to fragmented, non-interoperable platforms that burden data management. However, compatibility, usability, reliability, and usefulness of digital tools significantly enhance access to quality data and data-driven decisions. The study recommends strengthening and integrating existing systems and providing continuous digital health training to institutionalize data-informed decision-making.

Abstract Background Diabetic Retinopathy (DR) is one of the leading cause of vision loss and blindness. AI models have been instrumental in providing an alternative solution to real-life medical treatment which are costly and sometimes not readily available in developing and underdeveloped nations. However, most of the existing AI models are developed with high-quality clinical images that makes it difficult to use such models in low-resource settings. For this reason, this research focus on bridging this gap by developing a low-resource, mobile-friendly, and deployable deep learning (DL) model for the detection of DR using an imbalance-aware optimal transport (OT) learning approach. Methods We trained our proposed framework with both high-quality hospital- grade images and low-resource smartphone-acquired images, and evaluated with the original test set from the smartphone domain. We also curated three levels of smart- phone image-degradation quality and reported results from multiple experiments with bootstrapping. All model evaluations were assessed using the AUC, Sensitivity, and Specificity. Our results were compared with empirical risk minimization (ERM), Prototype OT, and Sinkhorn OT methods. Results We used four strong backbone architectures in the assessment. With our framework, Mobilevit-s achieved the best performance: an AUC of 87%, sensitivity of 89%, and specificity of 95%. Meanwhile, the statistical significance performance test (95% CI) shows that the AUC results are in the range of approximately 84% to 89%. For sensitivity, the range is 81% to 96%, and for specificity, 93% to 96%. This result indicated a performance increase of more than 3-5% compared to baseline methods. Conclusion Our framework shows promising results for low-resource DR screening, which has a potential to benefit less-advantaged groups and developing nations. Keywords Diabetic retinopathy, cost-effective AI, optimal transport, smartphone screening, deep learning.

Artificial intelligence (AI) is increasingly integrated into healthcare delivery, yet patient acceptance in resource constrained settings remains incompletely characterized. This study assessed attitudes toward AI supported care among patients attending hospitals in three Jordanian governorates (Amman, Balqa, Irbid) and examined demographic and digital literacy correlates of acceptance. In a cross sectional survey (n = 500 complete questionnaires), participants rated exposure to AI in healthcare and five attitudinal domains, namely perceived usefulness or performance expectancy, trust and transparency, privacy and perceived risks, empathy and human interaction, and readiness or behavioral intention, using 25 items on 5 point Likert scales. Patients expressed conditional optimism: empathy and human interaction was most strongly endorsed (M = 4.33, SD = 0.58), alongside relatively high perceived usefulness (M = 3.97, SD = 0.68), while trust and transparency (M = 3.57, SD = 0.74) and readiness (M = 3.66, SD = 0.90) were moderate to high; privacy and risk concerns were moderate (M = 3.51, SD = 0.77) and self reported exposure was lowest (M = 2.57, SD = 1.07). The highest agreement item indicated preference for AI to work alongside physicians rather than be relied on alone (M = 4.47, SD = 0.81). Trust and transparency and perceived usefulness were positively associated with readiness (r = 0.48 and r = 0.44, respectively; p <.001), while privacy and perceived risks were negatively correlated with trust and usefulness. In multivariable regression adjusting for gender, age group, education, prior AI health app or device use, and self rated digital skill, lower educational attainment (less than high school and high school) predicted reduced readiness, whereas higher digital skill predicted increased readiness (R2 = 0.101). These findings suggest that implementation strategies in Jordan should emphasize human involvement alongside AI, transparent communication and governance, and interventions that build digital confidence and reduce readiness gaps linked to education. Author summaryAI is increasingly used in healthcare, for example to support diagnosis, triage, and treatment decisions. Whether these tools are accepted by patients depends not only on how well they work, but also on whether patients trust them, understand how they are used, and feel their privacy is protected. Evidence on patient views in middle income and resource constrained settings is still limited. We surveyed 500 patients attending hospitals in three Jordanian governorates to understand how they view AI supported care. Patients generally expected AI to be useful, but they strongly preferred that clinicians remain actively involved and that AI supports rather than replaces physicians. Trust and perceived usefulness were closely linked to willingness to accept AI enabled care, while privacy concerns were present and shaped trust. Readiness to accept AI was lower among participants with lower educational attainment and higher among those with greater self rated digital skill. These findings suggest that successful implementation in Jordan should prioritize transparent communication, strong privacy safeguards, and human centered workflows, while also strengthening digital confidence to avoid widening gaps in acceptance.

Background: Mobile phone-based tools are increasingly used to collect data on non-communicable disease (NCD) risk factors, particularly in low-resource settings where traditional data collection systems face operational and infrastructural constraints. This study examined stakeholder perspectives on the use of enhanced mobile phone-based capabilities to support the collection of public health surveillance data on NCD risk factors in low-resource settings. Methods: An exploratory qualitative study was conducted between November 2022 and July 2023. Twenty in-depth interviews were conducted with public health specialists, ethicists, NCD researchers, health informaticians, and policy makers in Uganda. Thematic analysis was used to interpret the results. Results: Four themes emerged from the data, including benefits of using mobile phone capabilities for NCD risk factor data collection; ethical, legal, and social implications; perceived challenges of using such mobile phone capabilities; and proposed solutions to improve the utility of phone-based capabilities in data collection on NCD risk factors. Participants recognized the potential of mobile technologies to improve data collection efficiency and expand access to hard-to-reach populations. However, concerns emerged regarding inadequate informed consent, risks to privacy and confidentiality, unclear data ownership, and vulnerabilities created by inconsistent enforcement of data protection laws. Social concerns included low digital literacy, unequal access to mobile devices, and fear of stigmatization. Participants emphasized the need for transparent communication, robust data governance, and community engagement. Conclusion: Mobile phone-based systems can strengthen the collection of NCD risk factor data in low-resource settings; however, their benefits depend on addressing key ethical, legal, and social challenges. To ensure responsible deployment, digital health initiatives must prioritize participant autonomy, data protection, equity, and trust building. Integrating contextualized ethical, legal, and social considerations into design and policy frameworks will be essential to leveraging mobile technologies in ways that support inclusive and effective NCD prevention and control.

Tuberculosis (TB) is prevalent in Uganda and overlaps with a high rate of HIV/TB coinfection. While nearly all hospital-based TB cases in Kampala, the capital of Uganda, show clear TB symptoms, 30% or more of undiagnosed TB cases found through active screening are asymptomatic. Additionally, the host risk factors for TB in Kampala cannot be distinguished from environmental risk factors. These TB-specific challenges are just part of the complexity, especially in areas with high HIV/AIDS burden. Data science techniques, especially Artificial Intelligence (AI) and Machine Learning (ML) algorithms, could help untangle this complexity by identifying factors related to the host, pathogen, and environment, which are difficult to explain or predict with traditional/conventional methods. In this project, we will use health data science approaches (AI/ML) to identify factors driving TB transmission within households and reasons for anti-TB treatment failure. We will utilize the computational resources at Makerere University and available demographic, clinical, and laboratory data from TB patients and their contacts to develop AI and ML algorithms. These will aim to: (1) identify patients at baseline (month 0) unlikely to convert their sputum or culture results by months 2 and 5, thus at risk of failing TB treatment; (2) identify household contacts of TB cases who are at risk of developing TB disease, as well as contacts who may resist TB infection despite repeated exposure to M. tuberculosis. Achieving these objectives will provide evidence that data science methods are effective for early detection of potential TB cases and high-risk patients, thereby helping to reduce TB transmission in the community. The study protocol received approval from the School of Biomedical Sciences IRB, protocol number SBS-2023-495.