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PubMed2026

Workforce Representation in Ophthalmology Oral Board Examiners and Examinees.

PURPOSE: To evaluate the characteristics of the American Board of Ophthalmology (ABO) oral board examiners and examinees as well as trends in examiner and examinee gender over time. DESIGN: Retrospective cohort study. SUBJECTS: ABO oral board examiners and examinees. METHODS: We utilized data from the American Board of Ophthalmology (ABO) and Association of American Medical Colleges (AAMC) to assess board examiner and examinee demographic characteristics from 2011 to 2024. Examiner characteristics included gender, years of experience, initial certification year and average examiner score. Examinee characteristics included gender, years since residency graduation and exam outcome (pass/fail). We utilized logistic regression to examine temporal trends in examiner and examinee gender from 2013 to 2024. MAIN OUTCOME MEASURES: Gender distribution of examiners and examinees. RESULTS: Overall, the proportion of women examiners increased over the study period. Notably, the most pronounced increase occurred following the transition from in-person to virtual oral board exam administration in 2020, rising from 31.3% (95% CI: 24.1%-38.4%) in 2019 to 49.1% (95% CI: 43.8%-54.4%) in 2024 (p < .001). The percentage of women examinees remained stable (41.7% [95% CI: 37.6%-45.8%] in 2019; 37.5% [95% CI: 33.0%-42.0%] in 2024, p-value: .019). CONCLUSIONS: The representation of women among the ABO oral board examiners has increased significantly following the transition to virtual exams while the proportion of examinees remained stable. Strategies to increase flexibility in scheduling exams may be beneficial in continuing to improve examiner representation.

Humans↗

PubMed2026

Infertility treatment in women with epilepsy: A systematic review.

BACKGROUND: The impact of assisted reproductive technologies (ART) on seizure control in women with epilepsy remains incompletely understood. METHODS: A systematic review was conducted according to PRISMA guidelines. EMBASE, MEDLINE, CINAHL, Scopus, and the Cochrane Library were searched from inception to March 2025. Eligible studies included observational studies and case-based reports involving women undergoing infertility treatment. RESULTS: A total of 1216 publications were identified, of which four studies met the inclusion criteria, including case reports, a case series, and a cohort study. These studies included 16 women aged 25-46 years undergoing infertility treatment, all but one of whom had epilepsy. Interventions involved in vitro fertilization (IVF), ovulation induction, and hormonal therapies. Patients were treated with a range of antiseizure medications (ASMs), including carbamazepine, clobazam, lamotrigine, levetiracetam, oxcarbazepine, valproate, and zonisamide, either as monotherapy or in combination. Seizure frequency was generally stable, with most patients maintaining baseline seizure control. Seizure exacerbations were uncommon and primarily associated with hormonal therapy and reduced ASM levels, particularly reduced lamotrigine levels. Reported events included breakthrough seizures in the setting of decreased lamotrigine concentrations, seizure clusters associated with follitropin beta, and a new-onset seizure following dehydroepiandrosterone exposure. Across studies, multiple ART attempts resulted in live births with different ASM regimens, as well as in patients not receiving ASMs. CONCLUSION: Available evidence suggests that ART is feasible in women with epilepsy, with most patients maintaining stable seizure control. Hormonal therapy may affect ASM pharmacokinetics and seizure threshold, thereby warranting close monitoring. Larger prospective studies are needed to better define ASM-specific effects and optimize care.

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

Structured Visualization for Laparoscopic Skill Acquisition:A Randomized Controlled Study.

OBJECTIVE: To evaluate whether structured visualization can support acquisition of basic laparoscopic skills during simulation training and whether this approach can achieve outcomes comparable to repeated physical practice. DESIGN: Prospective randomized comparative study. SETTING: Simulation-based laparoscopic skills training at a university-affiliated teaching center. PARTICIPANTS: Fifty laparoscopy-naive medical students were randomly assigned to a laparoscopic practice group or a visualization group. The laparoscopic practice group performed a validated Gynecological Endoscopic Surgical Education and Assessment (GESEA) Laparoscopic Skills Training and Testing (LASTT) hand-eye coordination task 7 consecutive times. The visualization group performed the same task physically on attempts 1, 4, and 7, while attempts 2, 3, 5, and 6 consisted of guided visualization. Each attempt lasted up to 2 minutes, and performance was scored as the number of correctly placed rings (range 0-12). RESULTS: Baseline performance was comparable between groups (2.92 ± 2.14 vs 2.88 ± 1.72; p = 0.94). Both groups improved significantly over time (p < 0.001). No statistically significant between-group differences were found on the 4th attempt (6.52 ± 3.25 vs 5.76 ± 2.57; p = 0.48) or 7th attempt (8.24 ± 2.86 vs 7.44 ± 2.99; p = 0.39). The final physical performance of the visualization group was significantly better than the 3rd physical attempt of the laparoscopic practice group (p = 0.025). CONCLUSIONS: Structured visualization may support early laparoscopic skill acquisition and achieve short-term outcomes comparable to repeated hands-on simulator training. Visualization should be considered an adjunct, rather than a replacement, for physical practice in simulation-based laparoscopic education.

Laparoscopy↗

PubMed2026

Rational design of high-productivity perfusion processes for CHO Cells: From growth inhibitory strategies to model-driven optimization.

While perfusion culture for Chinese hamster ovary (CHO) cells offers advantages such as continuous operation and flexibility, it suffers from product loss through cell bleeding and difficulties in reaching high productivity due to sustained rapid cell growth. Growth inhibitory strategies are widely used to enhance productivity in fed‑batch processes; however, their practical implementation and comparative effectiveness in perfusion processes remain insufficiently explored. Meanwhile, process development often relies on costly trial‑and‑error approaches. Here, we systematically compared three growth inhibitory strategies in perfusion culture-low cell‑specific perfusion rate (CSPR), sodium butyrate, and mild hypothermia-with respect to cell growth, metabolism, productivity, and product quality. Genome‑scale metabolic flux sampling analysis revealed that low‑CSPR and sodium butyrate induce a convergent up‑regulation of energy metabolism, correlating with greater gains in specific productivity (qp). Building on this insight, we developed a growth‑kinetic model for the combined low‑CSPR + butyrate strategy, incorporating parameter uncertainty. This model‑guided framework enabled the rational design of two distinct high‑productivity perfusion processes: a sustained mode that achieved robust long‑term stability alongside substantial productivity gains, and a high‑intensity mode that pushed qp and daily volumetric titer to their maxima, with increases of up to 108.94% and 190.36%, respectively, in a model CHO cell line with a moderate baseline productivity. Our study provides a proof‑of‑concept framework for perfusion intensification, from strategy selection to rational process design.

Animals↗

PubMed2026

[Study of a patient with azoospermia due to variant of MOV10L1 gene].

OBJECTIVE: To explore the clinical and genotypic characteristics of a patient with Sertoli cell-only syndrome (SCOS) due to variants of MOV10L1 gene. METHODS: A 27-year-old patient with Non-obstructive azoospermia (NOA) underwent routine semen analysis. Serum levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), progesterone (P), estradiol (E2), prolactin (PRL), and testosterone (T) were determined by chemiluminescence assays. Peripheral blood samples were collected for G-banded karyotyping analysis. Multiplex PCR fluorescence detection was used to screen for AZF gene microdeletions. Whole exome sequencing (WES) and Sanger sequencing were performed simultaneously. Testicular biopsy tissues were subjected to Hematoxylin-Eosin (HE) staining to assess seminiferous tubule cell composition, and MOV10L1 protein expression was detected by immunohistochemical staining. Bioinformatics tools were employed to predict the pathogenicity of variants and their impact on protein structure and function. This study was approved by the Medical Ethics Committee of the Guangdong Institute of Reproductive Sciences [Ethics No.: 2023(01)]. RESULTS: The patient's two semen analyses had failed to detect any sperm. Hormone tests indicated elevated FSH (22.32 mIU/mL) and PRL (397.6 mIU/mL), while T (3.68 nmol/L) and E2 (38.32 pmol/L) were reduced. Chromosomal karyotyping revealed 46,XY, and no AZF gene deletion was detected. WES and Sanger sequencing detected compound heterozygous variants of the MOV10L1 gene, including a c.345C>A (p.C115X) nonsense variant and a c.3323C>T (p.T1108I) missense variant, with the former being unreported previously. HE staining showed only Sertoli cells in the seminiferous tubules, confirming the diagnosis of SCOS. Immunohistochemical staining revealed absent MOV10L1 protein expression in the testicular tissue. Based on the guidelines from American College of Medical Genetics and Genomics (ACMG), the c.345C>A (p.C115X) was classified as a pathogenic variant (PVS1+PM2_Supporting+PP4), while the c.3323C>T (p.T1108I) was deemed variant of uncertain significance (PM2_Supporting+PP3_Supporting+PP4). Bioinformatics analysis demonstrated that c.345C>A (p.C115X) may cause premature termination of protein translation, while c.3323C>T (p.T1108I) may disrupt the hydrophobicity of the RNA helicase domain, reducing the active pocket volume and decreasing its affinity for MILI protein. CONCLUSION: This study has diagnosed a case of SCOS due to compound heterozygous variants of the MOV10L1 gene, which also enriched its mutational spectrum.

Humans↗

PubMed2026

Simultaneous Administration of Human Papillomavirus (HPV) Vaccine With Other Recommended Vaccines Among Adolescents Aged 13-17 years, National Immunization Survey-Teen (NIS-Teen), United States, 2023.

PURPOSE: To investigate the percent of adolescents who receive human papillomavirus (HPV) vaccine with one or more other vaccines recommended for adolescents in a single medical visit. METHODS: Data from the 2023 National Immunization Survey-Teen were analyzed. Timing of receipt of HPV vaccine, tetanus, diphtheria, and acellular pertussis vaccine (Tdap), quadrivalent meningococcal conjugate vaccine (MenACWY), and influenza vaccine was assessed using provider-reported vaccination histories. RESULTS: In 2023, among adolescents aged 13-17 years, 69.5% received HPV vaccine with one or more other vaccines recommended for adolescents in a single medical visit. In addition, 47.8% received specifically HPV vaccine, Tdap, and MenACWY together in a single medical visit. DISCUSSION: The HPV vaccine is commonly given with other vaccines recommended for adolescents in a single medical visit. These findings demonstrate variation in simultaneous vaccination patterns, suggesting that flexibility in the recommended adolescent vaccination schedule allows for different approaches to vaccination across clinical settings and family preferences while maintaining adherence to the recommended schedule.

Humans↗

PubMed2025

Influence of soil types with different soil-forming process on the qualitative and quantitative detection of microplastics by near-infrared spectroscopy.

Microplastics (MPs) have become a pressing global environmental threat, with soils-acting as sinks for MPs from multiple sources-gaining increasing attention. Near-infrared (NIR) spectroscopy offers a promising tool for MPs detection due to its rapid, non-destructive, and field-applicable features. Although previous studies have focused on the effects of individual soil components on the NIR detection performance of MPs, there is still a lack of systematic research on how the complex background-formed by the coupling of multiple physicochemical properties in natural soils-affects detection performance. This study focuses on soil types with different soil-forming processes, selected five representative agricultural soils to systematically evaluate how the combinations of physicochemical properties they represented affect the performance of NIR-based qualitative and quantitative analysis of MPs in soils. The results demonstrated that soil type significantly affected both the spectral response and detection performance of MPs. Brown Pedocals and Brown Earth exhibited clearer characteristic absorption and stronger linear responses, achieving higher identification accuracy under low (<1.5 %) or zero MPs concentrations and the best quantitative performance (R2 ≥ 0.988, prediction set root mean square error (RMSEP) ≤ 0.110 %). In contrast, Phaeozem and Red Soil were more prone to misclassification at low concentrations, while Fluvo-aquic Soil showed the poorest quantitative performance. This study is the first to reveal, at a holistic level, the critical constraints posed by natural soil complexity on the NIR detection of MPs, offering targeted empirical evidence to support the application of NIR technology in real-world soil environments.

Soil↗

PubMed2026

Smartphone apps for obesity management: A systematic review using self-determination theory.

BACKGROUND: While bariatric surgery and pharmacotherapy are effective treatments for obesity, ongoing supportive care remains a challenge. Smartphone applications (apps) may assist with symptom management, but their effectiveness and practical use in obesity treatment is unclear. This review evaluated the effectiveness, acceptability, and feasibility of these apps in supporting individuals following obesity treatment. To better understand how these apps may promote sustained engagement and behaviour change, their design was analysed using Self-Determination Theory (SDT). METHODS: A systematic search was conducted across MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, SCOPUS, and CENTRAL databases. Eligible studies included randomised and non-randomised interventions involving adults (≥18 years) with obesity (BMI ≥ 30 kg/m2) who had undergone bariatric surgery or pharmacotherapy. Interventions had to include an app designed to support post-treatment symptom management. Findings were synthesised narratively, and app features were mapped to SDT constructs of autonomy, competence, and relatedness. RESULTS: Five studies (three RCTs, two cohort studies) involving 1,133 participants were included (female: 78 %; median age: 47.63 years). Most apps targeted post-bariatric surgery care; only one focused on pharmacotherapy. Common features included tracking, reminders, and education, supporting autonomy and competence. Relatedness features such as communication and peer support were least represented. Two studies reported improvements in weight-related outcomes and one in medication adherence. Effects on quality of life, self-efficacy, and healthcare utilisation were not significant. Patient satisfaction was reported in one study, with 95 % expressing positive feedback, though formal assessments of feasibility and acceptability were limited. CONCLUSION: Smartphone apps show potential to support obesity management, particularly after bariatric surgery. While some evidence suggests benefits for weight loss and adherence outcomes, the limited studies and variability of reporting prevent conclusive observations in other outcomes. Future app development should integrate behavioural theory to address psychological needs, nutritional risks and promote holistic self-management beyond weight control.

Female↗

PubMed2026

Artificial intelligence in genitourinary oncology: publication trends and systematic review.

OBJECTIVE: To conduct an analysis of publication trends and a systematic review of randomized controlled trials (RCTs) to characterize the current state of artificial intelligence (AI) use in genitourinary (GU) oncology, as AI has emerged as a transformative tool in healthcare with potential applications in diagnostics, treatment planning, and prognostication. METHODS: We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE; Ovid), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) Ultimate for studies related to AI and GU oncology, excluding non-English papers, non-human studies, review articles, and articles using AI solely for manuscript writing. Publication trends were analysed from 2013 to 2023 and categorized by study design and cancer type. RCTs were evaluated through systematic review using Covidence (Veritas Health Innovation Ltd, Melbourne, Victoria, Australia) for screening and data extraction. Two reviewers independently assessed all studies, with risk of bias (RoB) evaluated using the Cochrane RoB 2.0 tool. RESULTS: Of 2409 articles identified, 1220 met inclusion criteria. These included 962 retrospective articles, 175 prospective studies, 79 studies with combined retrospective/prospective methods, and four RCTs. Studies most commonly addressed prostate (n = 923), renal (n = 274), and urothelial (n = 194) cancers. Publications grew from 14 in 2013 to 362 in 2023, with substantial acceleration in 2019. Four RCTs were identified - one in urothelial cancer and three in prostate cancer. Two RCTs evaluated AI-based diagnostics, demonstrating improved performance over conventional methods; the remaining two RCTs evaluated AI in prognostication and treatment planning, showing improved gains in imaging interpretation and operational efficiency. RoB varied across studies, primarily related to randomisation and deviations from intended interventions. CONCLUSIONS: Artificial intelligence research in GU oncology has grown, although high-level evidence from RCTs remains limited. Existing trials underscore AI's promise in diagnostics, prognostication, and treatment planning, and the rapidly evolving nature of this field warrants continued prospective investigation.

Humans↗

PubMed2026

Access Block and Ambulance Ramping: The Canaries of the Healthcare System.

OBJECTIVE: To identify evidence-based factors leading to the global challenge of hospital access block and inform strategies to improve emergency access performance. METHODS: A mixed methods approach was followed comprising an umbrella review of published systematic reviews, qualitative analysis of the perspectives of patients and healthcare workers, and quantitative analysis of contextual factors and 6 years of ambulance, emergency inpatient and ward movement records for the 25 largest public hospitals in Queensland, Australia. RESULTS: A key set of findings and recommendations were identified to improve emergency access that are practical and actionable. These comprise the introduction of inpatient discharge metrics and monitoring to shift focus from the front door of hospitals to the 'back door'; increasing support for primary care, community care, aged care, NDIS and vulnerable groups; maintaining demand-side strategies such as increasing inpatient-equivalent care alternatives (e.g., hospital in the home, acute care within nursing home services); investment in prehospital flow; improving hospital processes such as extended-hour discharge lounges; improving workforce; and revising funding policies. CONCLUSIONS: The study findings fill a gap in the evidence regarding challenges and recommendations for improving patient flow within hospital emergency departments and across the broader health system. Focussing efforts at the 'back end' of the inpatient journey is a critical step to improve emergency care outcomes.

Humans↗

PubMed2026

Same-day initiation of tenofovir alafenamide-based pre-exposure prophylaxis with drug-level feedback for transgender women in Uganda.

OBJECTIVE: To evaluate the feasibility and acceptability of same-day initiation of emtricitabine/tenofovir alafenamide (F/TAF) pre-exposure prophylaxis (PrEP) and test the impact of drug-level feedback on PrEP adherence among transgender women (TGW) in Uganda. DESIGN: Randomized controlled trial. METHODS: HIV-negative TGW were randomly assigned 1 : 1 to intervention (drug-level feedback with tailored adherence counseling) or standard-of-care (SOC), and followed quarterly for 12 months (November 2021-July 2023; NCT04491422). Quarterly clinic visits included demographic and socio-behavioral data collection, PrEP refills, STI testing, and quarterly PrEP adherence assessment using tenofovir levels in dried blood spots (DBS; long-term) and urine (short-term). RESULTS: We enrolled 200 TGW (100 per arm), median age 21 years. Same-day F/TAF PrEP initiation was 100%. Tenofovir detection in urine (intervention arm) was 79, 80, 85, and 70% at the 3, 6, 9, and 12-month visits, respectively. Tenofovir detection in DBS was 46, 40, 35, and 31% at 3, 6, 9, and 12 months, respectively. Median tenofovir DBS concentrations were 40.6 and 47.0 fmol/punch in intervention and SOC arms, respectively. There was no intervention effect on PrEP adherence (DBS tenofovir levels) [adjusted incidence rate ratio (aIRR) 1.06; 95% CI: 0.82-1.37]. Never being harassed by police for being transgender (aIRR 1.66; 95% CI: 1.24-2.23), history of taking daily medication for more than 7 days (aIRR 1.51; 95% CI: 1.18-1.93) and higher monthly income (aIRR 1.44; 95% CI: 1.10-2.04) were associated with PrEP adherence. CONCLUSION: Oral F/TAF PrEP adherence among TGW in Uganda was low and not affected by drug-level feedback or tailored adherence counseling. Long-acting injectable PrEP formulations should be considered for this population.

Humans↗

PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗

PubMed2026

Clinical predictors of severe and fatal respiratory syncytial virus infection in adults and the elderly: A retrospective cohort study.

BACKGROUND: Respiratory syncytial virus (RSV) is increasingly recognized as a cause of severe respiratory illness in adults, especially the elderly and those with comorbidities. However, data on outcomes and risk factors for severe disease in this population remain limited. METHODS: We retrospectively analyzed 123 adult patients diagnosed with RSV infection at a tertiary center in Taiwan from 2015 to 2023. Clinical characteristics, laboratory data, detection of other pathogens, clinical course and outcome were reviewed. Multivariable logistic regression identified risk factors for severe RSV infection, including ICU admission and 30-day mortality. RESULTS: The mean age was 55.7 years; 50% were aged ≥60 years and 13% were ≥75 years. ICU admission occurred in 17%, with significant associations to viral coinfection and elevated C-reactive protein (CRP). Thirty-day mortality was 13%, and overall in-hospital mortality was 18%, all among patients with comorbidities. Independent predictors of 30-day mortality included late elderly (aOR 24.2, p = 0.03), high CRP > 11.5 mg/dL (aOR 16.4, p = 0.005) and thrombocytopenia < 34,103/μL (aOR 11.4, p = 0.01). CONCLUSION: Advanced age (≥75 years), high CRP, and severe thrombocytopenia are key predictors of mortality in adults RSV patients. These findings highlight the need for targeted prevention strategies, including vaccination, in high-risk populations.

Co-infection↗

PubMed2026

What Constitutes Effective Support and Provision Within Day Service Centres for People With Intellectual Disabilities? A Systematic Review of Qualitative Research.

BACKGROUND: This review aimed to investigate the effectiveness and quality of support and provision within day service centres for people with intellectual disabilities. METHOD: The International Bibliography of the Social Sciences, Scopus and PsycInfo databases were searched in August 2024, and the results were reported according to the PRISMA guidelines. Peer-reviewed, English-language, qualitative studies that investigated the effectiveness of day service provision for people with intellectual disabilities in non-residential settings were considered for review. Methodological quality of the included studies was assessed using the JBI Critical Appraisal Tool for qualitative research. Qualitative themes were identified through thematic analysis and synthesised using the ConQual approach. RESULTS: Fourteen studies were included and four key themes emerged: 'perceptions of service quality'; 'community-orientation, integration, and empowerment'; 'challenging behaviours and safety'; and 'staff-centred factors and job satisfaction'. Confidence in the evidence was 'very low' for 3/4 themes, while there was 'moderate' confidence in the evidence related to the theme 'perceptions of service quality'. CONCLUSIONS: Day service centres for people with intellectual disabilities may enhance their effectiveness and quality of provision by concentrating on promoting communication, engagement, relationships, social networks and community integration. Addressing the methodological shortcomings and incomplete reporting of related research in future would contribute to improvements in overall confidence in the evidence base. This can then be better used to inform and further enhance day service provision for people with intellectual disabilities.

Humans↗

PubMed2026

The future of precision oncology and artificial intelligence in Belgium: scenarios and policy responses.

PURPOSE: Precision medicine, also known as personalized medicine, enables the provision of tailored health services to patients. In the prevention, early detection, and treatment of cancers, precision medicine is highly promising, given the increasing use of genomic profiling for diagnosis and adapting therapies in several tumor types. Artificial Intelligence (AI) can support this process by analyzing vast amounts of relevant data. However, high-quality data and financial investments in the health system are essential for the implementation of precision medicine and AI solutions in routine cancer care. DESIGN/METHODOLOGY/APPROACH: Building on the quantitative outcomes of a foresight exercise published in another study, this article collects qualitative data to gain more detailed insights into the future of precision oncology in Belgium and discusses the role of AI in this field. It reports the results of a series of expert workshops, focusing on four hypothetical future scenarios that are centered around technological and economic issues that must be overcome for the widespread use of precision oncology in Belgium. FINDINGS: The study concludes that all four scenarios discussed in the workshops would require supportive policy measures in Belgium, which should go beyond mere technological and economic considerations, such as involving patient associations and the public in policy design or creating multi-disciplinary expert groups for precision medicine. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to employ foresight methodology to illustrate possible future scenarios, scrutinize feasible approaches for implementing precision oncology in Belgium, and discuss the use of AI in this context.

Belgium↗

PubMed2025

Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.

BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection. PURPOSE: A systematic review and meta-analysis was performed to assess whether AI improves clinicians' ability to detect fractures on radiographs. MATERIALS AND METHODS: A literature search was conducted in PubMed, Web of Science, and Cochrane Library for studies published between January 1, 2010, and October 10, 2025. A meta-analysis of diagnostic accuracy studies was performed using a Summary Receiver Operating Characteristic (SROC) curve. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Subgroup analysis and meta-regression were conducted to explore potential sources of heterogeneity. RESULTS: A total of 26 studies were included . The pooled sensitivity of clinicians increased from 77% (95% CI: 72-81) to 87% (95% CI: 83-90) with AI assistance, while the pooled specificity improved from 88% (95% CI: 85-90) to 92% (95% CI: 89-94). The corresponding AUC values were 0.90 (95% CI: 0.87-0.92) before and 0.95 (95% CI: 0.93-0.97) after AI assistance. Eight studies were rated as high risk of bias. Subgroup analysis and meta-regression identified potential sources of heterogeneity, including fracture location, AI model type, high risk of bias, and reference standards. CONCLUSION: AI assistance significantly improves clinicians' diagnostic performance in detecting fractures on radiographs for extremity and trunk fractures.

Humans↗

PubMed2026

Diagnostic performance of machine learning models for malignant and non-malignant pleural effusion: Systematic review and meta-analysis.

BACKGROUND: Accurately distinguishing malignant pleural effusion (MPE) from non-malignant pleural effusion is clinically important, but the generalisability and methodological quality of machine-learning (ML) models remain uncertain. METHODS: We searched eight databases to 23 April 2026. Diagnostic performance was pooled using random-effects and Reitsma bivariate models, and study quality was assessed using PROBAST+AI. RESULTS: Forty-two studies were included; 17 contributed to the AUC meta-analysis and 14 to the bivariate analysis. The pooled AUC was 0.90 (95 % CI 0.85-0.94; 95 % prediction interval 0.62-0.98), with sensitivity of 0.80 (95 % CI 0.77-0.83) and specificity of 0.87 (95 % CI 0.79-0.92). Only nine studies reported external, temporal or independent validation. Externally validated studies had a lower pooled AUC than studies without external validation (0.83 vs 0.92), with lower specificity observed in the two externally validated studies contributing sensitivity and specificity data. All 42 development assessments had high overall quality concerns, and all 42 model evaluations were judged at high risk of bias. CONCLUSIONS: ML models showed good apparent accuracy for distinguishing MPE from non-MPE, but the evidence was limited by substantial heterogeneity, high risk of bias and scarce external validation. The pooled estimates reflect the average performance of different selected models rather than the expected accuracy of a single clinical test. ML models should be regarded as adjuncts to existing diagnostic pathways until they are confirmed by rigorous multicentre prospective external validation and clinical-impact studies.

Humans↗