Frontiers in Digital Health

Background: Approximately 2.5 billion people require assistive products (APs), yet access remains limited partly because of shortages of trained personnel. eHealth tools may help strengthen workforce capacity and translate evidence into practice. The World Health Organization's Training in Assistive Products (TAP) program is an open-access eHealth tool designed to strengthen AP provision. However, its impact on learner outcomes remains insufficiently evaluated. This study assessed the usability and acceptability of the TAP module and changes in self-efficacy in mobility aid provision. Methods: We conducted a quasi-experimental pre-post study with health science students who completed the WHO TAP "Introduction to Assistive Products" module. We assessed usability using the System Usability Scale, acceptability using the Technology Acceptance Model and Theoretical Framework of Acceptability, and self-efficacy using an adapted version of Bandura's Multidimensional Scales of Perceived Self-Efficacy. We analyzed data using descriptive statistics and paired-samples t-tests. Results: Of 53 participants, 33 completed both assessments. Usability exceeded established benchmarks (76.97 +/- 9.66), indicating a positive user experience. Acceptability findings demonstrated favourable responses across domains, with high ratings for ease of use and intervention coherence, and comparatively lower burden scores. Mean self-efficacy increased across all competencies from 2.32 +/- 0.58 before the module to 4.23 +/- 0.36 afterward (p < 0.001), reflecting substantial gains in confidence in AP provision. Conclusion: The eHealth TAP module demonstrated above-average usability and favourable acceptability, while significantly improving self-efficacy in AP provision. These findings highlight the potential for eHealth training implementation to strengthen workforce capacity and bridge training gaps in global health.

Background: The uptake of new digital and AI based technologies in the health care setting, including digital diagnostics, has been comparatively slower than in other sectors. This is a consequence of the complex legislature, lack of routine procedures for the secondary use of sensitive medical data. Inadequate level of education and incentives for different stakeholders prevents overcoming strong barriers, such as clinical workflow integration, and need for comprehensive eval uation of utility of interventions which requires multi-disciplinary collaboration effort. The paper presents a Re-hospitalization Challenge, organised by the European Digital Innovation Hub (EDIH)- AI4Health.Cro, a co-creation activity and an effective tool for overcoming the mentioned barriers for implementation of new AI technologies and overall innovation ecosystem build-up. Methods: Crowdsourcing challenges or hackathons are R&D strategies in which ad-hoc assembled competing teams collaborate on early-stage technology devel opment or research problem solving. Re-hospitalisation challenge, in our case, has had multiple facets: as a co-creation activity tasked with construction of an AI predictive model with the explanatory support and plausible business model for its deployment; it involved also extensive organisers effort: data anonymisation, provision of secure processing environment, and comprehensive assessment of solutions. The paper details the challenge structure and processes, from problem inception to execution and comprehensive evaluation of developed solutions. Results: As a co-creation effort in digital healthcare, the challenge provided results on multiple levels: (i) through tailored solutions based on real evi dence data, serving as benchmarks or prototypes for later deployment in practice; (ii) through sandboxing EHDS principles to enable secondary data use; (iii) as an ecosystem building event yielding ad-hoc collaborative teams and cross-disciplinary participative learning experience. Conclusions: Crowdsourcing innovation challenges are valuable digital health care ecosystem building tool that provides a number of direct and indirect 2 impacts, from providing valuable diagnostic prototypes for innovation elicitation, creation and transformation of startups, to streamlining secondary use of clinical data for research and innovation. Overall impact is also enhanced through raising the awareness and education of all stakeholders relevant for the translation of AI technologies and into routine clinical setting.

Healthcare access barriers among Nigerian university students remain under-researched, particularly regarding localized digital health interventions. This study designed, developed, and evaluated a mobile health platform integrating AI-assisted triage, teleconsultation, appointment scheduling, and records management for students at the University of Abuja Teaching Hospital (UATH). A single-group pre-post design was conducted with 50 enrolled student-patients, with each participant observed for 30 days. The platform's triage feature utilized MedGemma, deployed locally via Ollama on UATH infrastructure using a multi-turn conversational architecture. Evaluation metrics included a survey operationalizing the Penchansky and Thomas access framework, the System Usability Scale (SUS), and triage validation against two independent clinicians using weighted Cohen's kappa. Among 61 in-scope triage sessions from 28 participants, mean triage response time was 34.8 seconds with no performance degradation. Among 22 participants completing the SUS, the mean score was 73.2, significantly exceeding the standard 68 benchmark (p = .046). Healthcare access improved directionally across four of five measured dimensions. AI triage classifications demonstrated substantial agreement with independent clinicians (kappa = 0.66-0.72), strengthening in longer conversations. No significant access equity differences were detected between on-campus and off-campus participants in the underpowered pilot subgroup analysis. This study demonstrates the technical feasibility, usability, and clinical concordance of a locally deployed, AI-assisted health platform in a resource-constrained Nigerian setting, providing, to our knowledge, the first evaluation of MedGemma for AI-assisted triage among Nigerian university students and justifying a larger, fully powered follow-up study.

Background. General purpose generative AI (GenAI) chatbots are increasingly used by students for mental health support. Research on prevalence estimates vary widely, rarely link use to validated clinical measures, and have not been reported in a Canadian student population. We estimated the prevalence trends, patterns, perceived impact, and correlates of GenAI use for mental health support among Canadian university students. Methods. We analysed one year (May 2025 to April 2026) repeated cross-sectional data from the Canadian arm of the WHO World Mental Health International College Student survey (WMH-ICS) The primary outcome was past-year prevalence of GenAI use for mental health support. Specific use purposes, perceived impact, reasons for non-use, and future use intent were also analysed. Factors associated with GenAI use were assessed using modified Poisson regression. As a sensitivity analysis, an elastic-net penalised regression model was fitted to assess the robustness of findings to an alternative modelling approach. Results. The past-year prevalence of GenAI chatbot use for mental health support was 25.2% (95% CI: 22.7 - 27.9), with a lifetime prevalence of 30.2%. Use was mostly occasional and predominately for seeking mental health information, stress management, and emotional support/companionship. Students of Asian ethnicity, those with higher clinical burden, recent adverse life experiences, weaker social support, and prior digital help-seeking behaviours were more likely to use GenAI for mental health purposes. Conversely, 2SLGBTQ+ students and those with romantic partners were less likely. Nearly three-quarters (74.2%) of users perceived such use to have a positive impact on their mental health and emotional wellbeing. Non-users reported preference for human interaction, distrust of GenAI in mental health (67.4% each), and privacy/security concerns (50.3%). Non-use also reflected principled objections to AI, including ethical and environmental concerns, with most non-users indicating no future use intention. Conclusions. GenAI chatbot use for mental health support has become commonplace among Canadian university students and is concentrated among those with greater mental health needs and fewer social support resources. Although most users perceived these tools as beneficial, their clinical effectiveness and safety remain uncertain. Rigorous prospective studies are needed to determine whether perceived benefits translate into improved mental health outcomes and whether purpose-built GenAI mental health interventions offer greater clinical benefit and safety than general-purpose chatbots.

Background. Post-assessment data processing, quantitative syndromic profiling, and clinical report generation in acute stroke settings typically require up to 50 minutes of manual documentation per patient, straining clinical workflows and driving clinician burnout. Conversely, standard automated screening tools (NIHSS, MoCA) lack sensitivity to the qualitative specificity of focal deficits and fail to generate structured, auditable clinical narratives. Methods. We performed a retrospective observational study of a prospective acute registry (n = 169; 84 males, 85 females; mean age 66.87 years, SD = 13.9, range 18-97). Cognitive mapping was executed via the NeuroDraft platform, which pairs a deterministic Python core implementing a 10-domain "Luria Raw" matrix (scaled 0-5) with a constrained generative linguistic layer for structured reporting. Results. The deterministic scoring pipeline demonstrated high internal consistency (Cronbach's alpha = 0.85). Kruskal-Wallis testing (df = 7, ties-corrected) revealed marked discriminant validity across baseline mental status tiers (p < 0.001), led by visual object perception (H = 44.86) and complex attention (H = 42.55). Principal component analysis of the covariance matrix identified a primary general deficit factor (lambda_1 = 4.13, explaining 41.26% of total variance) dominated by visuoconstructive functions (loading = 0.46), with basic numerical calculation showing relative independence (loading = 0.21). Depressive symptom loading demonstrated negative orthogonality to organic impairment severity (r = -0.20). Conclusion. The NeuroDraft software standardizes and quantifies qualitative Luria's syndromic analysis, mitigating human-error bias and reducing the medical reporting cycle to 5-10 minutes. This deterministic method provides reproducible cognitive profiling suitable for routine clinical workflows. Keywords: Ischemic stroke, cerebrovascular disease, post-stroke cognitive impairment, neuropsychological assessment, Luria's aphasiology, quantified process approach, automated clinical reporting, deterministic algorithms.

Objectives. To assess how digital inclusion factors and physical access barriers are associated with user trust in smartphone-based remote photoplethysmography (rPPG) hypertension screening, and to identify implications for digital health pol- icy, procurement and implementation in low-resource settings. Methods. Cross-sectional mixed-methods survey in five outpatient clinics in Kebbi State, northern Nigeria (N =287). Trust was measured using comfort, confidence and perceived usefulness Likert scales. Primary analyses used binary logistic models with HC3 robust standard errors; sensitivity analyses are reported in supplementary material. Free-text responses were thematically analysed. Results. Smartphone ownership was 51.2%; Transsion-brand devices comprised 56.5% of owners. Greater distance to a blood pressure facility was independently associated with lower perceived usefulness (OR 0.51, 95% CI 0.30-0.87; p=0.013) and lower comfort (OR 0.61, 0.37-0.98; p=0.042). Among owners, Transsion versus Samsung showed higher confidence odds (OR 3.82, 1.02-14.27; p=0.046). Qualitative themes supported the implementation interpretation: platform-fit and device speed requests among Transsion owners; connectivity and offline-first concerns among those with greater travel distance. No brand contrast achieved FDR-adjusted significance; brand findings are exploratory. Conclusions. Digital health policy and health technology assessment for smartphone-based screening should incorporate local device ecology, connectivity constraints, physical access burden and trust-calibration safeguards. Pre-implementation assessment of these factors is necessary for equitable and safe rPPG adoption in low-resource health systems.

Early identification of cognitive impairment remains challenging in settings where comprehensive cognitive and clinical assessments are not available. Acoustic and linguistic features in naturalistic speech may serve as useful behavioural markers of cognitive impairment, but the value of integrating these measures with cognitive assessment remains unclear. We tested whether combining acoustic and linguistic features from one-minute speech samples with multi-domain cognitive assessment (spanning attention, language, memory and executive functions) improves classification of cognitively unimpaired individuals from those with amnestic mild cognitive impairment or early-stage Alzheimer's Disease. Across multiple machine learning models, combining cognitive, acoustic and linguistic features yielded significantly better classification performance than models using cognitive or speech features alone (area under the curve = 0.96-0.98, both comparisons p < .05). This proof-of-concept study reveals that integrating speech-based measures with cognitive testing may improve identification of cognitive impairment, supporting the development of accessible and scalable multimodal screening tools for primary care.

Digital mental health tools are increasingly promoted as scalable supports for young people, yet implementation remains inconsistent, particularly for marginalised youth. Acceptability and usability are key determinants of successful adoption, but little is known about how these factors shape engagement across diverse youth populations. The aim of the study was to examine the acceptability, usability, and implementation potential of 11 evidence?based digital mental health tools among marginalised young people across the Republic of Ireland (ROI) and Northern Ireland (NI). A mixed?methods design integrated baseline surveys (n = 38), a two?week trial of digital tools delivered through a co?designed Google Site, online workshops/individual interviews (n = 22), and a final usability and engagement survey (n = 24). Usability was assessed using the System Usability Scale (SUS), engagement using the Twente Engagement with E?Health Technologies Scale (TWEETS), and mental wellbeing using the Short Warwick-Edinburgh Mental Well?Being Scale (SWEMWBS). Qualitative data were analysed thematically and mapped to the Consolidated Framework for Implementation Research (CFIR). Only two tools exceeded the SUS usability benchmark. Engagement was moderate overall, with one tool achieving the highest engagement despite lower usability. SWEMWBS scores indicated moderate baseline mental wellbeing. Thematic analysis identified five acceptability themes: credibility and trust; accessibility and ease of use; positive content supporting emotional regulation; personalisation and self?monitoring; and engagement and habit formation. CFIR analysis highlighted usability, institutional trust, cultural relevance, and emotional needs as core implementation determinants. Digital literacy was high and supported engagement, and usability remained a critical gateway to implementation. Designers and commissioners of digital mental health tools should ensure that supports are simple, trustworthy, culturally relevant, and youth?centred to enable adoption among marginalised young people. Implementation strategies are needed that will co?design with diverse youth communities and prioritise youth work settings as well as governance clarity.

Introduction: Heart failure care requires coordination across hospital and community settings, yet information is often fragmented across electronic medical records and clinical systems. Clinical dashboards can bring together and display information to support care delivery and service management; however, existing dashboards have generally focused on specific measures, interventions and monitoring pathways. This study aimed to co-design, iteratively develop and user-test an integrated heart failure dashboard that links patient-level clinical decision-making with service-level management. Methods: A human-centred design approach comprising needs identification, collaborative ideation, iterative prototype development and end-user testing was applied across two complementary operational and clinical dashboard streams. Thirty-six clinicians, health service managers, data and implementation scientists, and consumers from metropolitan and regional services participated. Results: For operational decision-making, participants prioritised real-time visibility of patients across heart failure services, patient trajectories, service-performance information, and identification of variation in guideline-directed care and outcomes. Clinical priorities included rapid synthesis of longitudinal information, optimisation of guideline-directed medical therapy, continuity across care settings, and clinical workload prioritisation. These requirements informed the dashboard prototypes. Many prioritised information elements were incompletely represented in structured data and distributed across disconnected systems and structured and unstructured clinical data sources. Conclusion: Human-centred co-design identified complementary patient- and service-level information needs and translated them into linked dashboard prototypes. The proposed dashboard brings together current clinical status and longitudinal heart failure care history at the patient-level alongside service-level patterns. Implementation is required to evaluate whether these linked views improve care processes and patient outcomes.

Background and Objective: Lung function reference equations are essential for interpreting spirometry and related modalities, yet existing tools are fragmented, modality- and language-specific (predominantly R), and lack integrated severity classifiers. We present PySpiro, an open-source Python library unifying 23 spirometry, 1 multiple breath washout, 2 diffusing capacity, 1 static lung volume, and 2 oscillometry reference equations under a single API, with 10 clinical severity classifiers. Methods: PySpiro was implemented in Python 3 using NumPy and pandas, with all equations coded directly from literature. The unified API exposes predicted medians, z-scores, limits of normal (LLN/ULN), and raw Lambda-Mu-Sigma (LMS) triplets per parameter, plus a vectorised compute() method for batch processing at the DataFrame level. Unit-tests covering every equation and classifier verify agreement with published values and confirm correct boundary, out-of-range, and missing-input handling. Results: PySpiro covers spirometry equations from 1971 to 2022 across Caucasian, Asian, Indian, and race-neutral global populations, plus non-spirometry modalities (multiple breath washout, diffusing capacity, static lung volumes, oscillometry). Classifiers span COPD, interstitial lung disease, pattern and severity grading, bronchodilator reversibility, and primary ciliary dyskinesia. Compared with rspiro (5 equations, 4 spirometry) and the only prior Python package (spiref, 2 equations, no z-scores or % predicted), PySpiro offers substantially broader coverage, multi-modality support, a richer output API, and 10 classifiers absent from both. Conclusions: PySpiro is freely available (PyPI: pyspiro; doi:10.5281/zenodo.15519193) and provides a comprehensive, validated, and extensible platform for lung function data analysis in research pipelines.

Background. Federated learning enables collaborative model development without transferring patient level data between institutions. However, translation into routine healthcare practice remains limited because deployment requires more than distributed model training. Federated infrastructures must operate within secure processing environments, satisfy governance and security requirements, support auditability, and integrate with existing regulatory workflows for secondary use of health data. Although federated learning has been demonstrated in multiple clinical applications, evidence for its deployment, assessment, and operation within regulated health-data infrastructures remains limited. Methods. We implemented a decentralised swarm learning system for federated learning and integrated it into Acamedic, a certified secure processing environment (SPE) operated by Helsinki University Hospital (HUS). Introduction of the swarm learning coordination layer constituted a material modification to the previously certified HUS SPE and triggered a differential regulatory security assessment under Finland's Act on the Secondary Use of Health and Social Data and associated Findata requirements. The assessment evaluated controls relating to data isolation and locality, identity and access management, logging and monitoring, network security, and environment protection, while establishing a governance model that separates certification of infrastructure-level controls from study specific assessment of data, models, parameter exchanges, and outputs. The swarm learning architecture was developed and deployed across the university hospitals of Helsinki, Turku, and Tampere, with HUS serving as the regulator-assessed implementation within a certified SPE and partner sites operating under local institutional governance frameworks. As an operational exemplar, we trained federated DeepSurv survival models for acute myeloid leukaemia (AML) using harmonised longitudinal laboratory data. Findings. The differential security assessment identified one high-severity, two medium-severity, and two low-severity findings. All high- and medium-severity findings were remediated and verified, after which an independent certification report was issued for the assessed extension. The resulting federated-learning capability was authorised for secondary use of health data and operationalised as a reusable extension to the HUS secure processing environment. Swarm learning was successfully executed across three university hospitals without transferring patient-level data, achieving 100% parameter merge success. Training operations were traceable through infrastructure logs, container logs, and a distributed ledger coordination layer recording participant registration, parameter exchanges, and model-training lineage. On independent test sets from all three hospitals, the swarm-trained AML model demonstrated stronger risk stratification than locally trained reference models, with consistently stronger risk separation, higher log-hazard ratios (swarm vs local: 4.32 vs 1.79 at HUS, 7.20 vs 3.19 at TAYS, and 5.90 vs 2.91 at TYKS), and improved discrimination metrics. The resulting federated-learning capability was authorised for secondary use of health data, operationalised as a reusable extension to the HUS secure processing environment, and made available for future permit approved analyses. Interpretation. This study demonstrates that federated learning can be integrated into a certified secure processing environment, subjected to regulatory assessment, and operated across independent hospitals without centralising patient level data. The principal contribution is a reusable governance and infrastructure model that separates assessment of system-level controls from study specific evaluation of data, models, parameter exchanges, and outputs. By enabling federated learning to function as an assessed infrastructure capability rather than a project-specific exception, the approach provides a practical pathway for operationalising decentralised federated learning within regulated health-data environments. The findings are directly relevant to emerging European frameworks for secondary use of health data, including secure processing environments envisioned under the European Health Data Space, whose implementation builds on many of the same governance principles evaluated in this study.

Background. Designing high-quality Objective Structured Clinical Examination (OSCE) stations is a time-consuming process. Generative artificial intelligence (AI) represents a promising path to accelerate content creation by automating the generation of scenarios. A growing number of AI tools is now available for this purpose. Objective. To assess the variability between generative AI models in their ability to produce OSCE stations in the field of paediatrics. Methods. A structured prompt was developed based on the French national OSCE guidelines for medical education. Five distinct AI models were provided with this prompt, alongside the neonatal jaundice chapter from the French pediatric reference textbook, to generate 6 complete OSCE stations. Results. Prompt compliance was high for ChatGPT 5.1, ChatGPT 5.2, Gemini 3.0 Pro, and Claude Opus 4.5, while it was lower for Grok 4.1. Expert-rated quality was generally high, with few factual errors or missing information across models. However usability differed significantly between models. This was also true for several quality dimensions such as checklist clarity, embedding of checklist answers within vignettes, and ease of standardized patient formation. ChatGPT 5.1 required the most revisions and Gemini most often rated usable as is. Significant inter-model differences were observed in diagnostics, only with ChatGPT 5.1 sampling all three neonatal jaundice categories. Contextual variables showed systematic narrowing across models. Clinical grid density was consistent (10-12 items per station), but thematic distribution differed markedly. Soft skills coverage varied significantly across models (p=0.002), none of them consistently representing all communication competency domains. Conclusion. Large language models can generate structurally compliant OSCE stations, but surface compliance conceals substantive inter-model differences in diagnostic coverage, contextual diversity, and soft skills representation, that compromise content validity. No model currently meets the criteria for unsupervised deployment in a summative assessment bank. The choice of model carries pedagogical implications and expert curation remains essential before integration into high-stakes assessment workflows.

Background: Accurate patient understanding of lung cancer screening (LCS) results is critical for engagement and follow-up adherence, given the currently low screening uptake. While artificial intelligence (AI) systems show promise in facilitating patient communication, its effective use in clinical settings requires appropriate invocation of imaging tools and self-regulation by deferring certain questions to physicians. To this end, we developed and evaluated a planner-executor style multimodal agentic system to answer simulated patient questions about LCS CT results. Methods: This retrospective study utilized 116 LCS CT reports and images collected from a tertiary academic hospital. The system employed a multi-agent architecture (planner and executor) to mimic clinical reasoning. We developed a large language model (LLM)-based question generation framework to prepare a comprehensive set of 699 simulated patient questions, categorized as answerable and defer-to-doctor to assess self-regulation. Performance was evaluated on tool-calling, self-regulation accuracy (ability to correctly defer out-of-scope questions to a human provider) assessed by LLM-as-a-judge, and clinical quality assessed by a reader performance study with four physicians evaluating a subset of 100 responses. Results: The system demonstrated high overall tool-calling accuracy of 93.1% (646/694; 95% CI: 91.2%, 95.0%) and self-regulation accuracy of 92.6% (462/499; 90.2%, 94.8%). For answerable questions, the percentage of responses receiving perfect 5.0 scores across all readers included 78.9% (180/228) for clinical accuracy (inter-rater agreement: Gwet's AC2=0.92), and 73.2% (167/228) for patient understandability (0.86). For defer-to-doctor questions, the percentage of responses included 77.3% (133/172) for clinical accuracy (0.80) and 70.3% (121/172) for patient understandability (0.78). Conclusions: The planner-executor style multi-modal agentic system reliably answers patient-specific questions about LCS. Its high tool-calling and self-regulation performance demonstrates its potential as a safe and effective digital communication facilitator in LCS programs.

Language discordance can impede community-based research and health communication where trained interpreters are limited. Although multimodal artificial intelligence systems can provide real-time spoken translation, performance with under-resourced languages during spontaneous field interactions remains poorly characterized. We evaluated ChatGPT-4o during bidirectional English-Nepali voice translation in a community setting near Dhulikhel Hospital, Nepal. In this cross-sectional field study, 30 primarily Nepali-speaking adults were recruited by convenience sampling. ChatGPT-4o mediated conversations using standardized English questions and spontaneous Nepali responses. A bilingual Nepali-English reviewer assessed 485 translated utterances using a 3-point accuracy scale and an inductively developed framework for translation and conversational deviations. Of 485 translations, 282 (58.1%) received the highest accuracy rating, 134 (27.6%) a moderate rating, and 69 (14.2%) the lowest. Mean accuracy was higher for English-to-Nepali than Nepali-to-English translation (2.63 {+/-} 0.53 vs 2.23 {+/-} 0.86); 63 of 69 low-accuracy translations (91.3%) occurred in the Nepali-to-English direction. Among 329 deviation tags, the most frequent were distortion of intended meaning (17.1%), overly formal or unnatural phrasing (14.7%), omission (14.2%), and addition of content (11.5%). Some fluent outputs substantially altered meaning or introduced information not expressed by the speaker. ChatGPT-4o demonstrated potential for real-time English-Nepali communication but also produced errors that could alter interpretation of participant responses. Accuracy was lower and more variable for Nepali-to-English translation; however, translation direction was confounded with input type because Nepali inputs were spontaneous and English inputs standardized, limiting conclusions about directional performance. These findings support cautious use for low-stakes conversational exchange and human verification when errors could affect research validity, clinical decisions, or participant understanding. As multimodal AI evolves, performance should be reevaluated across languages, real-world conditions, and model versions, with bilingual oversight and community partnership remaining central to responsible use.

Ambient artificial intelligence scribes are being increasingly used in healthcare to improve efficiency and reduce provider clinical documentation burden, yet their performance across linguistically diverse patient populations is not well characterized. We conducted a retrospective analysis of 54,160 outpatient encounters within a U.S. safety net health system to evaluate the performance of an artificial intelligence documentation tool in English and non-English clinical encounters, and in encounters where an interpreter or bilingual provider was present. Documentation performance was measured by the percentage of words in the final note that were generated by the ambient AI documentation tool and not edited by the provider. Associations between language factors and documentation performance were measured using Generalized Estimating Equations with exchangeable correlation structures to account for clustering of multiple encounters within unique patients. Univariable models were fitted to estimate the odds of adequate performance by language and interpreter modality, and a multivariable interaction model was used to evaluate within-language differences between bilingual providers and interpreter-mediated encounters. Non-English encounters were 21% to 25% less likely than English encounters to achieve the same performance threshold. There was no significant difference in generative documentation performance between interpreter-mediated and bilingual provider encounters. These findings underscore the importance of equity-focused evaluation and multilingual model refinement to ensure that artificial intelligence documentation benefits are distributed fairly across diverse patient populations.

Evidence-based medicine (EBM) concepts are difficult for medical students to grasp. We developed a Python-Streamlit web application providing interactive visualizations to enhance EBM education. Preliminary use with first year medical students demonstrated high engagement and improved conceptual understanding, supporting the feasibility of integrating interactive, web-based tools into EBM curricula.

As digital self-triage becomes more widely used, quality assurance requires evaluation of both emergency recognition and overall agreement with clinical judgement, including how these outcomes differ across generations of large language models. The Benchmark Self-Triage (BeST) project intends to provide a framework to compare systems for digital self-triage against a medical 'gold standard' in Germany. In a first step developing the method and demonstrating its potential we developed a representative set of patient vignettes and compared two regulated medical-device triage systems, SmED and Infermedica, with five structured OpenAI configurations spanning multiple model generations: gpt-4.1, gpt-5.4, gpt-5.6-luna, gpt-5.6-sol, and gpt-6-astra. An LLM-based patient actor covered 480 physician-curated vignettes derived from German routine triage data. A pool of seven physicians provided preliminary reference classifications and consensus for 480 vignettes. Recommendations were mapped to three urgency categories 'emergency', 'acute', and 'not urgent' and evaluated using winsorised weights derived from routine triage frequencies. Weighted emergency sensitivity ranged from 51.3% (95% CI 35.5-66.3%) for gpt-4.1 to 99.9% (99.7-100.0%) for gpt-6-astra. Weighted exact three-category agreement (accuracy) ranged from 54.4% to 68.8%; gpt-4.1 achieved the highest agreement despite the lowest emergency sensitivity. No system performed best across all urgency categories. The later-generation OpenAI configurations showed a more precautionary triage profile resembling SmED, with higher sensitivity for emergency cases; gpt-6-astra showed the strongest shift. This cross-sectional comparison across model generations suggests a shift towards more precautionary triage behaviour, without establishing a longitudinal improvement in accuracy. These findings highlight the limitations of overall agreement for evaluating self-triage and support category-specific assessment. Broader expert consensus and evaluation, as well as further steps to reduce dependence on SmED-derived source data, are needed and planned before drawing conclusions about clinical safety.

Objective. Triage of rheumatology outpatient referrals is a high-volume administrative task that consumes senior specialist time without advancing patient care. The human triage system is only moderately accurate and reproducible. We assessed whether contemporary large language models (LLMs) are able to perform well enough to support automating this task in practice. In addition, the effects of different prompting techniques on triage accuracy and cost was assessed to help identify to optimal approach. Methods. Twenty referral scenarios spanning the urgency spectrum, based on real referrals were created by a certified Australian rheumatologist. Four rheumatologists triaged all cases independently and blinded, to produce a consensus reference standard. Twenty-three LLMs each triaged every referral into one of five urgency categories, three times (1380 outputs per condition). The experiment was run with a simple prompt and repeated with a advanced prompt supplying explicit triage expectations and worked examples. Results. All 2760 attempts returned valid categories. Under the simple prompt, performance separated into distinct tiers, larger models were more accurate (Spearman rho=0.42; P=.047) and accuracy tracked cost. Advanced prompting minimised between-model variance in accuracy 5.3-fold (0.014 to 0.003; Levene P=.01), abolished the size-accuracy association (rho=-0.05; P=.83) and removed the accuracy-cost relationship. Leading models matched expert consensus on most cases, within or above the range reported for human triage. Under-triage errors persisted with some LLMs. Conclusion. Contemporary LLMs categorise rheumatology referral urgency as well or better than published human triage systems. Advanced LLM prompting methods substitute for the reasoning capability of larger models, suggesting that LLM performance on this task may not require the most expensive models. The tools to automate this administrative task appear to already exist. Strong candidate LLMs that might serve a production ready solution have been identified.

At-home sampling can address obstacles to participation in transcriptomics research. We previously developed the homeRNA and homeRNAmax platforms; these custom-designed tubes containing RNA stabilizer interface with a blood tube compatible with upper-arm blood collection devices, allowing for blood collection and RNA stabilization to occur from a participant's home. In this work we introduce homeRNAdrop, an insert for a commercially available blood tube (BD Microtainer) that divides the tube into two compartments: the top compartment holds 200 L blood and the bottom compartment holds ~590 L liquid stabilizer (RNAlater). By combining blood collection and RNA stabilization into the same tube, homeRNAdrop streamlines sample preparation for participants and allows for more compact sample storage for the lab. In a remote pilot study, n=25 participants from across the United States tested the homeRNAdrop kit. The n=23 samples successfully received by the lab show that samples collected using homeRNAdrop are of sufficient RNA quality (all RINs > 6, a common cutoff for analysis) and yield (mean yield=1.44 g) for downstream gene expression analysis. Almost all participants who successfully collected blood using the Tasso Mini device found the homeRNAdrop device easy to use (n=23/24). Overall, this work adds a simplified, user-friendly platform to the growing suite of tools for remote blood RNA stabilization.

Continuous, noninvasive blood pressure monitoring remains an unmet clinical need, particularly in the intensive care unit (ICU) where hemodynamically unstable patients need high-frequency monitoring. Invasive arterial catheterization represents the current standard of care for continuous blood pressure (BP) monitoring, but it carries risks and limits patient mobility. In this study, we evaluate the MOSAIC system, a novel multi-modal, multi-nodal wearable, wireless sensor system placed on multiple locations on the body, for continuous noninvasive BP estimation in a cohort of ICU patients. Unlike existing continuous BP sensors, the MOSAIC system offers an ideal form factor for continuous BP monitoring, enabling a fully untethered setup which minimally impacts activities of daily living. Leveraging sensor-derived biosignals to compute continuous BP, we determine the accuracy of our BP regression models using arterial line-derived blood pressure reading as a ground truth. Using a Light gradient boosted machine (LGBM)-based regression model, we demonstrate strong beat-to-beat agreement with a mean absolute error (MAE) of 5.66 +/- 5.94 mmHg for systolic BP (SBP) prediction and 2.45 +/- 2.87 mmHg for diastolic BP (DBP) prediction, and average ratio variability (ARV) of 0.527 +/- 0.185 and 0.489 +/- 0.170 for SBP and DBP, respectively, compared to linear and deep-learning regression baselines. Our findings demonstrate strong agreement between the predicted BP values and invasive, arterial-line BP measurements, supporting the feasibility of wearable, wireless, and cuffless blood pressure monitoring in high-acuity clinical settings.