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PubMed2026

The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis.

BACKGROUND: Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures. METHODS: Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC). RESULTS: Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively. CONCLUSIONS: Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.

Humans↗

PubMed2026

Extended Reality Interventions for Osteoarthritis of the Knee and Recovery After Total Knee Arthroplasty: Systematic Review and Meta-Analyses.

BACKGROUND: Nonpharmacologic interventions are important for treating knee pain due to osteoarthritis or after total knee arthroplasty (TKA), and extended reality (XR) technology may enhance treatments for these indications. OBJECTIVE: This systematic review aimed to evaluate XR interventions for pain due to knee osteoarthritis (KOA) or for recovery after TKA. METHODS: Databases were searched through May 2023 and updated in December 2025. Eligible trials evaluated XR interventions to treat KOA pain or after TKA. We classified interventions by depth of immersion and clinical mechanism. We used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria to determine the certainty of evidence for prioritized outcomes. Meta-analyses were performed when ≥3 studies evaluated similar comparisons, outcomes, and time points. RESULTS: Eligible trials addressed KOA (k=12) or recovery after TKA (k=9). Sample sizes ranged from 36 to 306 participants, and most studies had a follow-up of ≤3 months. Nineteen studies assessed pain-related functioning and pain intensity, and 5 assessed adverse events (AEs). For KOA, 10 studies examined interactive digital rehabilitation (IDR), and 2 examined virtual reality (VR)-digitally augmented exercise (DAE). IDR for KOA may result in better pain-related functioning (low certainty of evidence [COE]; pooled standardized mean difference [SMD] -0.59, 95% CI -1.11 to -0.06; prediction interval [PI] -1.72 to 0.55; k=5) and lower pain intensity at 6-8 weeks (low COE; pooled SMD -0.46, 95% CI -0.92 to 0.00; PI -1.39 to 0.47; k=4). VR-DAE for KOA (k=2) produced inconsistent results (very low COE). For post-TKA studies, 5 examined IDR, 2 examined VR-DAE, 1 examined VR-distraction, and 1 examined VR-psychoeducation. Post-TKA IDR may result in better pain-related functioning (low [k=4] and moderate COE [k=1]) but little to no difference in pain intensity (low-moderate COE; pooled SMD at 3-4 months -0.12, 95% CI -0.75 to 0.52; PI -1.63 to 1.27; k=3). VR-psychoeducation probably results in lower pain at 4 weeks (moderate COE; k=1), and VR-distraction may result in 6 months (low COE; k=1), whereas VR-DAE produced mixed findings (k=2; very low COE). IDR was not associated with AEs, and VR may not be associated with AEs for KOA (high and low COE), though AE reporting was uncommon (k=5) and evidence was very uncertain for post-TKA. CONCLUSIONS: IDR may augment treatment for KOA and post-TKA recovery, and VR may benefit post-TKA rehabilitation. This review is the first to stratify by level of immersion, clinical mechanism, and follow-up duration and to systematically evaluate AEs. IDR may be ready for integration into KOA care, while use after TKA needs more evidence. Randomized controlled trials with implementation outcomes could determine how XR interventions can be used for KOA, whereas trials evaluating efficacy and AEs are needed before their use for post-TKA.

Humans↗

PubMed2026

Manual, digital, and AI tumour-infiltrating lymphocyte scoring: a secondary analysis of the APHINITY randomised trial.

BACKGROUND: Stromal tumour-infiltrating lymphocytes (sTILs) are prognostic in early-stage HER2-positive breast cancer, but their role in the context of dual HER2 blockade remains undefined. We evaluated manual, digital, and artificial intelligence (AI)-based sTIL quantification, together with AI-derived spatial metrics, for prognostic and treatment-benefit stratification using tumour samples from the phase 3 APHINITY trial. METHODS: In the APHINITY trial, 4805 patients were randomly assigned to receive chemotherapy plus trastuzumab with pertuzumab or chemotherapy plus trastuzumab with placebo. Median follow-up was 74·1 months (IQR 68·3-75·4). We analysed 4262 haematoxylin and eosin-stained images using manual assessment, an automated digital approach, AI-based lymphocyte quantification (AI percentage lymphocytes), and two AI-derived spatial features (AI-TIL and immune hotspot). Interobserver reproducibility was assessed in 262 randomly chosen tumour samples scored independently by five pathologists. Multivariable Cox models were used to assess associations between TIL levels and invasive disease-free survival (primary outcome in APHINITY), distant recurrence-free interval, and overall survival. The heterogeneity of pertuzumab benefit was evaluated using subgroup analyses, subpopulation treatment effect pattern plot analyses, and nested Cox models with treatment-by-biomarker interaction terms. FINDINGS: Manual scoring showed high interobserver reproducibility (intraclass correlation coefficient 0·84 [95% CI 0·79-0·88]). Concordance between manual and automated methods was modest. AI-based scoring (AI percentage lymphocytes) reclassified 120 (11·6%) of 1035 node-positive tumours from immune-low (by manual scoring) to immune-high; this subgroup of patients showed greater separation of 5-year invasive disease-free survival curves between pertuzumab and placebo groups compared with patients whose tumours were concordantly classified as immune-low by both manual and AI-based approaches. Higher levels of TILs were associated with improved invasive disease-free survival for all sTIL measurement approaches and spatial measurements (hazard ratios [HRs] 0·41-0·93). Pertuzumab was associated with improved invasive disease-free survival at higher sTIL levels across all measurement approaches (HRs 0·36-0·48), but was not associated with higher values of spatial measures. The largest 6-year absolute improvements with pertuzumab were observed in patients with node-positive disease whose tumours scored in the highest level of immune infiltration of manual sTIL scoring (≥70·0%; mean absolute improvement 12·1 percentage points [SD 2·8]). In nested prognostic and predictive models, AI-based immune hotspot scores provided the most consistent additional information when combined with any sTIL measurement (all p<0·010). INTERPRETATION: Standardised manual sTIL scoring was reproducible, and digital and AI-based methods showed consistent prognostic stratification and potential for treatment-benefit stratification despite only modest correlation between platforms. AI spatial metrics provided complementary information beyond sTIL density and could support more scalable immune assessment. Future studies are needed to validate these approaches in independent cohorts and to clarify their clinical utility for stratifying contemporary HER2-directed therapies. FUNDING: None.

Humans↗

PubMed2026

Adverse Experiences in Brief Meditation Practices: Randomized Controlled Trial.

BACKGROUND: Meditation has become increasingly popular in recent decades. However, relatively little remains known about the prevalence of and risk factors for adverse experiences related to a single meditation practice. OBJECTIVE: The objective of our study was to examine adverse experiences associated with 3 brief, digitally delivered meditation practices (mindfulness, self-compassion, and gratitude) relative to using the internet as usual, as well as to investigate whether preintervention characteristics could predict such outcomes. METHODS: In a secondary analysis of a randomized controlled trial using samples that were representative of the US and UK adult populations with regard to ethnicity, sex, and age, we examined adverse experiences associated with 3 brief (ie, 5 or 10 minutes) meditation practices (ie, mindfulness, self-compassion, and gratitude) relative to using the internet as usual. We also investigated the potential of using preintervention characteristics to predict such outcomes. RESULTS: A total of 5049 participants completed all preintervention measures and were randomly assigned to meditation or control conditions. Across the sample, 4.1% (204/4925) of participants reported having a distressing experience during the intervention, and 7.1% (348/4908) of participants experienced an increase in negative affect from before to after the intervention. The results showed that participants who were randomized to a brief meditation intervention were no more likely to report a distressing experience than those who were randomized to use the internet as usual (odds ratio [OR] 1.05, 95% CI 0.76-1.47; P=.76). The results also showed that participants who were randomized to a brief meditation intervention were less likely to report clinically relevant increases in negative affect relative to using the internet as usual (OR 0.63, 95% CI 0.50-0.80; P<.001). Notably, participants in the 10-minute condition had a significantly higher likelihood of reporting a distressing experience than those in the 5-minute condition (OR 1.42, 95% CI 1.07-1.89; P=.02). Preintervention characteristics showed acceptable discrimination ability to predict a distressing experience (area under the curve=0.73) and slightly lower ability to predict increased negative affect (area under the curve=0.67). CONCLUSIONS: Taken together, we found that the brief, digitally delivered meditation practices tested in this study carry risks of adverse experiences that are comparable to or lower than those of typical activities on the internet; 10-minute condition was more likely to result in distressing experiences than 5-minute condition; and adverse responses to a brief meditation practice can, at least to a certain degree, be predicted using preintervention characteristics. TRIAL REGISTRATION: Open Science Framework 94HKS; https://osf.io/94hks/overview.

Humans↗

PubMed2026

Association of media use with sleep of children and adolescents: an umbrella review.

Adequate sleep is essential for child and adolescent development, driving extensive research across scientific disciplines. This umbrella review provides a comprehensive overview of existing evidence on media consumption and sleep and thereby lays the foundation for identifying key concepts and gaps. An inclusive systematic search for reviews reporting literature searches was conducted in 02/2021 and updated last in 09/2024. Methodological quality of the included reviews was assessed using AMSTAR-2. We included 84 reviews reporting on the association between media use and sleep in individuals aged 0-18 years. The field is dominated by reviews of low methodological quality, mainly including original cross-sectional studies with subjective measures in older children and adolescents. A total of 475 original articles were covered by the reviews; only 10 of them appeared in at least seven and at most nine reviews. Screen time generally had a negative impact on sleep, though evidence varied from very low to strong. Evidence for effects of conventional books on sleep remains inconclusive. High quality systematic reviews are needed to evaluate robust studies using objective measures of sleep and contemporary media use across all age groups up to adolescence and to explore the impact of non-digital media use on sleep, considering age and gender differences.

Humans↗

PubMed2026

School-based sexual violence prevention: A systematic review.

PURPOSE: Sexual violence profoundly affects the health and development of children, adolescents, and young adults, representing a persistent challenge to public policy. This systematic review examined the effectiveness of school-based interventions aimed at prevention. METHODS: Eighteen randomized controlled trials published between 2012 and 2024 were retrieved from four major databases. The programs were implemented in primary, secondary, and higher education settings and targeted children, adolescents, and young adults. RESULTS: The results revealed improvements in knowledge and attitude, particularly regarding consent and awareness, whereas evidence supporting behavioral changes was less frequent and often limited. Methodological limitations, such as short follow-up periods and participant attrition, restricted the assessment of long-term outcomes. CONCLUSIONS: This review highlights the importance of multicomponent, participatory, and culturally sensitive approaches, along with the integration of digital tools and continuous evaluation systems, to strengthen the role of schools as safe and transformative spaces in the prevention of sexual violence. IMPLICATIONS AND CONTRIBUTIONS: This systematic review suggests that school-based interventions hold significant potential for the prevention of sexual violence. It identifies promising strategies and reinforces the importance of culturally sensitive, sustained, evidence-based approaches to ensure learning environments that are safe, protective, and promotive of gender equity.

Humans↗

PubMed2026

Smartphone Apps for Preventing Adolescent Health Problems Among Health Care Professionals: Systematic Search and Quality Assessment.

BACKGROUND: Health care professionals must consider multiple dimensions of prevention when consulting with adolescents. Identifying risky behaviors early in adolescence is crucial for reducing both morbidity and mortality. General practitioners are increasingly eager to incorporate digital tools for prevention into their consultations with adolescents; however, the relevance and clinical validity of these digital tools are not always established or well-known. Consequently, primary care professionals require guidance and support in selecting relevant mobile health (mHealth) tools. OBJECTIVE: The aim of this study is to identify relevant and useful digital apps to help primary care professionals detect at-risk adolescents across all recommended areas of prevention: orthopedics, mental health, substance abuse, risk behaviors, sexual health, vaccinations, social relationships, and nutrition. METHODS: A systematic review of smartphone apps, with an analysis of content quality, was carried out by 4 researchers using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. The App Store and Google Play Store platforms were surveyed. The inclusion criteria were as follows: free of charge, date of last update, availability in French or English, relevance of the preventive approach to adolescents, and scientific validation. Four health care professionals assessed the apps: 2 selected the apps relevant to health care professionals, then 3 analyzed these apps using the French version of the Mobile App Rating Scale (MARS-F). Intraclass correlation coefficient, model (2,1) (2-way random effects, absolute agreement, single measures); standard error of measurement; and mean absolute error were also calculated. RESULTS: A total of 976 apps were identified, 49 of which had disappeared from the platforms prior to analysis. Nine apps were retained. Seven (0.72%) were included after evaluation using the MARS-F: 2 on mental health and 5 on sexual health (including 3 on contraception only). The mean MARS-F interrater score ranged from 2.5/5 to 3.8/5. The global MARS-F score demonstrated a pooled SD of 0.60 and an intraclass correlation coefficient (2,1) of 0.0003, resulting in a calculated standard error of measurement of 0.60. The average discrepancy between raters was a mean absolute error of 0.53. CONCLUSIONS: No similar studies have been identified in the literature that specifically focus on mobile apps designed to support health care professionals in delivering preventive care to adolescents. Of the 8 areas of prevention identified as relevant for adolescents, only 3 are addressed by the apps validated through our methodology (5 focus on sexual health). Consequently, current apps are insufficient to support health care professionals in their overall preventive work with adolescents. Such a review should be conducted systematically prior to the development of any new tool to prevent duplication and channel creative efforts toward truly innovative digital solutions. Furthermore, a thorough analysis of relevant, recommended websites is essential, as these resources complement the use of mobile apps designed for health care professionals.

Humans↗

PubMed2026

Routine methods misidentify Serratia spp.: Limitations of MALDI-TOF MS revealed by whole-genome sequencing.

Accurate species-level identification within the genus Serratia remains challenging due to extensive phenotypic overlap and high genomic relatedness among closely related and recently described taxa. This study presents an evaluation of routine and genome-based identification approaches applied to clinical Serratia isolates, integrating phenotypic assays, MALDI-TOF MS (Bruker Daltonics), 16S rRNA gene sequencing, and Whole-Genome Sequencing (WGS). A total of 103 isolates collected from a teaching hospital were analyzed. WGS was performed on a subset of isolates. Conventional biochemical methods classified all isolates as Serratia marcescens, whereas MALDI-TOF MS identified 60.1% as S. marcescens, 11.6% as S. ureilytica, and 28.1% just at the genus level. Peak analysis from MALDI-TOF MS revealed specific peaks associated with S. marcescens and S. ureilytica, but limited discriminatory power. WGS of six isolates initially identified as S. ureilytica by MALDI-TOF MS revealed reclassification as Serratia sarumanii (n = 5) and Serratia montpellierensis (n = 1), supported by Average Nucleotide Identity (ANI), Average Amino Acid Identity (AAI), and Digital DNA-DNA Hybridization (dDDH) thresholds. In contrast, 16S rRNA analysis showed limited species-level resolution. Phylogenomic and SNP-based analyses confirmed these classifications with strong support. Overall, this study underscores the critical role of high-resolution genomic approaches for precise species identification and highlights the need for continuous expansion and curation of MALDI-TOF MS reference databases to support reliable clinical diagnostics and epidemiological surveillance of emerging Serratia species.

Spectrometry, Mass, Matrix-Assisted Laser Desorpti↗

PubMed2026

Lifestyle interventions to prevent gestational and type 2 diabetes among migrant women from low- and middle-income countries: a systematic review.

Migrant women from low- and middle-income countries (LMICs) living in high-income settings experience disproportionately high risk of gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM). This review aimed to identify and synthesise culturally adapted lifestyle interventions for preventing or managing GDM and T2DM among migrant women from LMICs, focusing on intervention components, cultural adaptation strategies, and behavioural and metabolic outcomes. Five databases (PubMed, Embase, Scopus, CINAHL, Cochrane Central) were searched using Preferred Reporting Items for Systematic reviews and Meta-Analysis 2020 guidelines. Eligible studies included experimental designs involving lifestyle interventions delivered to migrant women from LMICs in high-income countries, reporting outcomes related to GDM or T2DM targeting behaviour change. Data were synthesised narratively; study quality was appraised using RoB2 for RCTs and a structured narrative approach for non-randomised designs. Certainty of evidence was evaluated using GRADE. Eight studies met the inclusion criteria. Sample sizes ranged from 28 to 641 participants. Intervention duration varied from 6 weeks to 12 months. Most interventions incorporated atleast one culturally tailored component, such as bilingual delivery, culturally adapted dietary education, or community-based engagement. Improvements were reported across dietary behaviours, physical activity, glycaemic measures, or diabetes-related knowledge; however, effect sizes were modest and inconsistent. Interventions combining dietary modification, physical activity, and culturally adapted delivery demonstrated greater improvements than exercise-only or digital-only programmes. Overall certainty of evidence ranged from low to moderate. Culturally adapted, multi-component lifestyle interventions show promise for improving behavioural and metabolic outcomes among migrant women from LMICs; however, the evidence base remains limited.

Humans↗

PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗

PubMed2026

BRAIN-Diabetes: Acceptability of an adapted FINGER multidomain intervention among adults living with type 2 diabetes in rural border regions across the island of Ireland.

BackgroundIndividuals with type 2 diabetes mellitus (T2DM) face increased risk of cognitive decline and dementia. Multidomain lifestyle interventions offer a non-pharmacological strategy to support brain health in this high-risk group.ObjectiveThis study examined the acceptability of a culturally adapted FINGER-based intervention among adults living with T2DM in rural border regions of Ireland (BRAIN-Diabetes Trial).MethodsA 6-month pilot randomized controlled trial was conducted. The intervention group received a multidomain program targeting diet, physical activity, and computerized cognitive training (CCT). The control group received standard care. Acceptability was assessed using questionnaires (all participants) and semi-structured interviews (intervention participants). Quantitative data were analyzed descriptively and qualitative data using template analysis, guided by four a-priori themes: trial participation and engagement, dietary behavior change, exercise behavior change, and CCT behavior change.ResultsQuestionnaire data (intervention: n = 28; control: n = 36) indicated high overall acceptability. Dietary and exercise components were rated most positively, while CCT component was less well received. Interviews (n = 25) highlighted facilitators to trial engagement, including perceived health improvements, and social connection, with time constraints and limited personalization as barriers. Dietary change was supported by tailored guidance but hindered by cost and availability. Facilitators for exercise included accessible resources and perceived benefits, with barriers including competing priorities. CCT engagement was mixed, with challenges including digital access and repetitiveness.ConclusionsThe Brain-Diabetes intervention was acceptable and feasible among adults with T2DM. Personalized support and accessible resources were key to engagement. Future work should refine delivery to enhance scalability and long-term adherence among high-risk groups.

Humans↗

PubMed2026

The effects of fitspiration TikTok content on body image and mood among young adult women in the U.S.

Fitspiration is an appearance-based form of media that promotes physical fitness and dieting. While not true of all fitspiration media, some forms promote these ideals through visuals of toned, athletic bodies that have become increasingly prevalent on the short-form video platform, TikTok. Although often framed as health-promoting, fitspiration exposure has been associated with upward appearance comparison (i.e., comparison with people perceived as more attractive) and negative effects on body image and mood. The present study investigated the effects of short-form video-based fitspiration on social comparison, appearance importance, appearance anxiety, body dissatisfaction, and negative affect. Using an experimental design, 150 undergraduate women (Mage = 19.28) in the United States were randomly assigned to view a five-minute TikTok compilation of either animal (n = 75) or fitspiration videos (n = 75). Participants completed baseline measures prior to viewing and state-level measures after completing their video set. Results indicated that, relative to control, viewing fitspiration content led to greater social comparison, appearance concerns, feelings of being fat, and sadness. Baseline appearance concerns and depressive symptoms significantly moderated group differences in responses, such that negative fitspiration effects on state-level appearance concerns were found among individuals high but not low in baseline appearance concerns and among individuals low but not high in baseline depressive symptoms. These findings contribute to the growing literature on fitspiration by demonstrating the immediate psychological effects of this content in short-form videos and highlighting the importance of considering individual differences in vulnerability.

Humans↗

PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗

PubMed2026

Association between Kidney Tubular Secretory Clearance with Cognitive Function among Adults with CKD in the Systolic Blood Pressure Intervention Trial.

BACKGROUND: Persons with CKD are disproportionally affected with cognitive impairment, yet the pathophysiology linking the two conditions is unclear. Because kidney tubule secretion is essential for clearance of medications, uremic toxins, and metabolites, we hypothesized that worse tubular secretion would be associated with reduced cognitive function in CKD. METHODS: The Systolic Blood Pressure Intervention Trial tested a systolic blood pressure target <120 mmHg vs. <140 mmHg in hypertensive individuals at high cardiovascular risk. In paired blood and urine specimens from 1,937 participants with eGFR <60 ml/min/1.73m2, we measured 10 endogenous tubule-secreted metabolites and calculated a urine/plasma ratio for each, then averaged these to generate a summary secretion score. We used unadjusted and multivariable-adjusted linear regression and mixed models to evaluate cross-sectional and longitudinal associations of the secretion score with the Montreal Cognitive Assessment, Digit Symbol Coding, and Logical Memory immediate and delayed tests-measured at baseline and months 24 and 48 of follow-up. Multivariable Cox regression evaluated associations with incident probable dementia and mild cognitive impairment, adjudicated by prespecified criteria. RESULTS: Mean age was 73 ± 9 years, 41% were women, mean eGFR was 48.2 ± 11.4 ml/min/1.73m2, median albuminuria was 14.8 [7.1-48.6] mg/g. Lower secretion score was associated with a 0.06 higher adjusted logical memory delayed score (95% CI: 0.02, 0.11) but not with other cognitive tests at baseline or longitudinal cognitive decline. After a median 4.1 years of follow-up, 118 developed probable dementia and 187 developed mild cognitive impairment. Each 1-SD lower secretion score was associated with lower risk of probable dementia (HR 0.78, 95% CI: 0.63, 0.98) but not mild cognitive impairment. CONCLUSIONS: Among Systolic Blood Pressure Intervention Trial participants with CKD, lower estimated tubular secretion was not associated with worse cognition at baseline or during longitudinal follow-up.

Journal Article↗

PubMed2026

Assessing perinatal depression identifying abilities among maternal and child health workers in rural China using smartphone-based virtual patients: a multi-center cross-sectional study.

OBJECTIVE: To assess rural maternal and child health (MCH) workers' virtual patients (VPs)-assessed performance in identifying perinatal depression (PND) using smartphone-based VPs, and to identify factors associated with this performance in rural Hunan, China. METHODS: A multicentre cross-sectional study was conducted in Hunan Province, China. A standardized questionnaire collected demographic and work-related characteristics of rural MCH workers. Smartphone-based VPs were used to assess PND identification performance in a simulated clinical scenario. An overall score ≥60 was used as a prespecified operational benchmark across consultation, ancillary assessment, diagnosis, management, and health education domains. Data were analyzed using SPSS 26.0. RESULTS: A total of 375 rural MCH workers participated, yielding an effective response rate of 90.4%. Only 25.9% met the prespecified operational benchmark for VP-assessed PND identification performance. The mean accuracy scores for consultation, ancillary assessment, diagnosis, management, and health education were 94%, 48%, 64%, 58%, and 74%, respectively. Complete consultation accuracy was higher among MCH workers from township health centers than among those from county-level MCH hospitals. MCH workers aged 18-39 years showed higher odds of complete diagnostic accuracy for PND than those aged ≥40 years. CONCLUSIONS: Smartphone-based VP assessment was feasible in rural MCH settings and revealed suboptimal PND identification performance. Mobile VPs may help identify frontline performance gaps and inform targeted training, but further validation against real-world clinical performance, or standardized patient encounters is needed before large-scale implementation. These findings may support targeted capacity-building for rural MCH workers and more equitable perinatal mental health care.

Humans↗

PubMed2026

Associations between smart infusion pump-electronic health record interoperability and healthcare outcomes: A systematic review.

OBJECTIVE: This study synthesized available evidence on the associations between smart infusion pump-electronic health record (EHR) interoperability and healthcare outcomes. METHODS: A systematic review of PubMed, CINAHL, Embase, and Scopus databases identified 901 records, which were imported into Rayyan® for duplicate removal, independent screening by three reviewers, and resolution of discrepancies. Eligible studies were peer-reviewed, data-driven, and reported associations between smart infusion pump-EHR interoperability and healthcare outcomes. Studies focused solely on technical validation or interoperability prototypes were excluded. A backward citation search identified additional studies. Two reviewers independently extracted and cross-validated study characteristics using standardized templates. Methodological quality was assessed with the Joanna Briggs Institute Critical Appraisal Tools. RESULTS: Twenty records of 14 full-text studies and 6 conference proceedings were included. Most records reported positive associations between smart infusion pump-EHR interoperability and outcomes related to safety (e.g., medication administration errors, safety-reported events, pump alerts, and compliance with interoperability and drug library), operational efficiency (e.g., programming and documentation time and technical issues), financial performance (e.g., charges captured, and cost avoided), and user experience domains. Most studies used observational designs, reflecting real-world interoperability implementations, where controlling confounding factors is challenging. Limited reporting of baseline characteristics, pump type, and sample sizes limited comparability across studies. CONCLUSIONS: Smart infusion pump-EHR interoperability was associated with improvements in patient safety, efficiency, charge capture, and user experience, with variable findings across studies. Future research should use rigorous methodologies and standardized measures, examine relationships across outcome domains, assess limitations of pump-EHR interoperability, and evaluate underexplored outcomes, including team communication, cognitive workload, and AI-enabled pumps. IMPLICATIONS FOR CLINICAL PRACTICE: Interoperability should be viewed as a component of a broader sociotechnical system, in which technology, user, workflow, clinical content, and organizational practices collectively determine overall effectiveness.

Humans↗

PubMed2026

Precision targeting of teacher burnout using network-informed ecological momentary interventions.

Teacher well-being affects classroom functioning and workforce stability, yet generic digital programs rarely use person-specific affect dynamics to select support. This cluster-randomised trial evaluated whether micro-interventions selected from high expected influence (EI) nodes in teachers' contemporaneous affect networks produced larger changes in burnout-related EI and everyday happiness than content-matched random allocation. The objectives were to estimate allocation effects on changes in estimated network summaries and happiness, evaluate network change as a statistical mediator, examine personality moderation, and benchmark simpler allocation rules. A two-arm cluster randomised platform trial was conducted in 84 public schools across four urban districts in H Province. After a 14 day baseline of ecological momentary assessment (EMA), person specific partial correlation networks were estimated for happiness, exhaustion, detachment, efficacy and rumination. An optimisation engine prioritised three brief micro-intervention types per teacher according to baseline EI, while the active control received the same library without network information. EMA continued for 8 weeks; Bayesian multilevel models, permutation-based mediation, and benchmarking analyses were applied. EI-based targeting produced larger reductions in the composite EI-change index than active control (mean difference 0.11, 95% credible interval 0.08 to 0.14) and higher week 7 EMA happiness (4.4 points on a 0 to 100 scale, 95% credible interval 2.7 to 6.0), with a positive arm by week slope difference of 0.62 points per week (95% credible interval 0.39 to 0.85). Model-based mediation estimates were consistent with approximately one half of the happiness difference being statistically associated with change in the composite EI-change index (average conditional mediation estimate 3.5 points, 95% credible interval 2.0 to 5.2). Benchmarking showed smaller gains under severity, threshold, or group-level centrality rules. Effects were stronger among teachers higher in conscientiousness. The findings indicate that integrating EMA, network modelling, and EI-driven optimisation yields measurable gains beyond content-matched exposure, providing a proof of concept for district-scale precision mental health that requires prospective implementation testing. Replication in additional regions, expanded node sets, and longer follow up are warranted to assess durability and generalisability.

Female↗

PubMed2026

BMT4me En Español: Multisite Feasibility and Usability Testing of a Spanish-Language mHealth Adherence Support App for Spanish-Speaking Caregivers of Children After Hematopoietic Stem Cell Transplantation and Cancer Treatment.

BACKGROUND: Medication nonadherence during the first 100 days after pediatric hematopoietic stem cell transplantation (HSCT) and during oncology treatment increases risk for complications. BMT4me is a caregiver-facing mobile health (mHealth) application providing medication reminders, symptom tracking, and note-taking features to support medication management. Spanish-speaking caregivers are frequently excluded from digital adherence interventions due to the lack of language-accessible tools. PROCEDURE: We conducted a multisite, mixed-methods usability testing of a Spanish-language version of BMT4me ("BMT4me en Español") with Spanish-speaking caregivers of children (ages 2-17 years) post-HSCT or with an oncology diagnosis on active treatment. Caregivers completed a facilitated, three-step usability session (unobtrusive observation, interactive observation, and debriefing), followed by a semi-structured interview, and then completed the system usability scale (SUS). Quantitative outcomes were summarized descriptively; qualitative data were analyzed using content analysis with constant comparison. RESULTS: Fifteen participants enrolled at each site for a total of 30 participants. Across both sites, the recruitment rate was 91%. All participants completed all parts of the study. The SUS score (M = 80.09; SD = 17.35) was above average (>68). Two key qualitative themes emerged: (1) the perceived positive impact of BMT4me on managing a serious illness and (2) the acceptance and sociocultural relevance of BMT4me for Spanish-speaking families. Caregivers also shared suggestions to add educational content and multiuser functionalities to BMT4me. CONCLUSIONS: The acceptance and perceived positive impact of the Spanish BMT4me app indicates that socioculturally relevant, Spanish mHealth interventions have strong potential to support Spanish-speaking caregivers in pediatric oncology and HSCT settings. CLINICAL TRIALS NCT: NCT06361173.

Adolescent↗