SEARCH · Search PubMed

Results for “Automation”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

8 recordsLinked to original sources

PubMed2026

Alarms and alarm management with automated versus conventional ventilation in neurocritical care patients.

INTRODUCTION: False or clinically irrelevant alarms are a major driver of ICU alarm fatigue and nursing workload. Ventilator alarms make up a large share, and although automated ventilation modes can reduce manual adjustments, their effect on alarm burden is still unclear. This issue can be particularly relevant in neurocritical care patients, where precise ventilator and alarm management is imperative for patient safety. OBJECTIVES: This explorative post hoc analysis of a randomized clinical trial compared alarm frequency and management between automated ventilation and conventional ventilation in neurocritical care patients. METHODS: Ventilator alarms and manual ventilator changes were captured continuously from the ventilator for up to 24 h per patient. The primary endpoint was a composite of workload-relevant alarms; with alarm management interventions at the ventilator as a key secondary outcome. Additional endpoints included redundant alarms, alarm duration and ventilator management. RESULTS: 13 patients received automated ventilation and 24 received conventional ventilation. No difference was observed in workload-relevant alarm frequency between automated and conventional ventilation (3.28 [2.87 to 4.30] vs 3.73 [1.66 to 7.33] alarms per hour; P = 0.81), while alarm management interventions at the ventilator were lower with automated ventilation (0.14 [0.10 to 0.15] vs 0.21 [0.17 to 0.31] interventions per hour; P = 0.01). Other alarm frequencies, duration of alarms and ventilator management were similar. CONCLUSIONS: In this exploratory post hoc analysis of a randomized clinical trial in neurocritical care patients during the early phase of mechanical ventilation, automated ventilation did not reduce the frequency of total or workload-relevant alarms, nor their duration, but was associated with fewer alarm management interventions compared to conventional ventilation. IMPLICATIONS FOR CLINICAL PRACTICE: Automated ventilation may not reduce alarm frequency in neurocritical care patients, but the observed reduction in alarm-related bedside interventions suggests a potential benefit for nursing workload.

Humans↗

PubMed2026

Design, rationale, and baseline patient characteristics for the Sickle Cell Disease and CardiovAscular Risk-Red cell Exchange (SCD-CARRE) trial.

BACKGROUND: Despite wide utilization of automated red blood cell exchange (RBCX) transfusion in adult patients with sickle cell disease (SCD), no consensus or quality efficacy data exist on its use. The Sickle Cell Disease and CardiovAscular Risk- Red cell Exchange (SCD-CARRE) trial tests the hypothesis that an automated chronic RBCX transfusion strategy reduces acute health care encounters and death while improving quality of life and end-organ function (cardiac, pulmonary and renal) in participants with SCD that are at high risk of death. METHODS: Adult patients with SCD with elevated tricuspid regurgitant jet velocity (TRV) and/or chronic kidney disease were considered to be at high risk of death and were randomly assigned to RBCX plus standard of care vs standard of care alone. Participants assigned to RBCX received 12 months of exchange transfusions to maintain target pretransfusion hemoglobin S% < 30%, post-transfusion hemoglobin S% < 20%, and post-transfusion hemoglobin concentration ≥10 g/dL. All study participants were managed according to NHLBI/ASH/ATS Expert Panel guidelines. The primary endpoint was the number of SCD acute health care encounters or death over 13 months. Secondary endpoints included measures of cardiovascular and renal function, exercise capacity, patient reported outcomes (all collected at baseline, and months 4, 8, and 12), and transfusion-related adverse events (collected monthly). RESULTS: Between 2020 and 2025, the SCD-CARRE trial randomized 173 participants at 23 sites across 3 countries. Enrolled participants had mean (SD) age of 45.8 (11.8) years and 54% were female. At baseline, participants had average TRV of 2.8 (0.5) m/s such that 45.9% had a TRV between 2.5 to 2.9 m/sec and 28.1% had a TRV ≥ 3.0 m/sec. The median (Q1, Q3) eGFR in this cohort was 60 (36, 110) mL/min/1.73 m2. The median (Q1, Q3) 6-minute walk test distance was 375 meters (309, 439), the median daily steps were 3,728 (2,187, 5,821), and participants experienced a median (Q1, Q3) of 2 (1, 5) pain episodes in the year prior to randomization. The trial results are pending. CONCLUSIONS: The SCD-CARRE trial successfully enrolled a cohort of n = 173 adults with SCD. This study highlights a rationale to evaluate the effect of automated chronic RBCX transfusion strategy plus standard of care as compared to standard of care alone in SCD patients at high risk of death with a focus on patient centered outcomes, preservation of cardiovascular function, end-organ complications and death. TRIAL REGISTRATION: ClinicalTrials.gov, Identifier: NCT04084080, https://clinicaltrials.gov/study/NCT04084080.

Adult↗

PubMed2026

How AI-supported intelligent systems support infection prevention and control training in healthcare: A systematic review of educational functions and outcomes.

AIMS: Artificial intelligence (AI)-supported intelligent systems have been increasingly incorporated into infection prevention and control (IPC) education and training, primarily to support the monitoring of observable behaviors and the provision of feedback. However, existing evidence has focused largely on short-term compliance outcomes, with limited synthesis of the educational role of AI-supported intelligent systems in supporting sustained IPC competence. This systematic review examined how AI-supported intelligent systems have been designed and used to support IPC education and training, with a focus on system characteristics, educational functions, and reported outcomes. DESIGN: A systematic literature search was conducted across the PubMed/MEDLINE, Embase, Cochrane, and CINAHL databases. DATA SOURCES: A total of 18 studies met the inclusion criteria. Findings were qualitatively synthesized according to system design characteristics, educational functions, and outcome domains. REVIEW METHODS: Methodological quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: Most AI-supported intelligent systems focused on hand hygiene and relied on fully automated monitoring systems to capture behaviors and provide performance feedback. Educational functions were predominantly limited to performance assessment, automated feedback, and reminders. Outcomes were mainly measured using compliance or performance metrics, whereas sustained behavioral change and decision quality were rarely assessed. CONCLUSIONS: AI-supported intelligent systems have been used primarily to reinforce short-term IPC performance and compliance. However, their current applications for supporting sustained competence over time remain limited. The findings of this review suggest that AI-supported intelligent systems may serve as maintenance-oriented educational support by extending learning beyond initial instruction through repeated practice and feedback. Future research should prioritize outcome measures that capture the durability of performance and decision-making processes to better align AI-supported intelligent systems used in IPC education and training with the educational demands of clinical practice.

Humans↗

PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗

PubMed2026

Combining neuromelanin-sensitive MRI and quantitative susceptibility mapping for enhanced diagnosis and differentiation of parkinson's disease: A systematic review.

BACKGROUND: Loss of dopaminergic neurones and iron deposition in the substantia nigra pars compacta (SNpc) are two major pathological hallmarks of Parkinson's disease (PD). Such changes can be visualised by advanced techniques including neuromelanin-sensitive MRI (NM-MRI) and quantitative susceptibility mapping (QSM). This systematic review investigates the diagnostic performance and methodological development of the integrated use of NM-MRI and QSM in PD. METHODS: The systematic search was performed in four databases (Scopus, PubMed, ScienceDirect, and Web of Science) according to the PRISMA 2020 guidelines until July 2026. Bias was assessed using QUADAS-2 and certainty of evidence was assessed using GRADE. RESULTS: Seventeen studies with 2228 participants were included. Combined NM-MRI and QSM consistently showed reduced neuromelanin volume/contrast and increased iron deposition in the SNpc of PD patients compared to healthy controls. Multimodal integration yielded a significant improvement in diagnostic accuracy (AUC values 0.86-0.99), and was able to successfully differentiate PD. Recent methodological advances included simultaneous acquisition sequences (e.g. MTC-GRE, STAGE, setMag) and AI-driven automated segmentation, which led to significantly reduced scan times and improved reproducibility. CONCLUSION: The combination of NM-MRI and QSM has a synergistic effect and provides powerful complementary biomarkers for the diagnosis and differential diagnosis of PD.

Humans↗

PubMed2026

Artificial intelligence-derived myocardial fibrosis on cardiac magnetic resonance for prognosis in cardiomyopathy: A systematic review of a sparse evidence base.

BACKGROUND: Myocardial fibrosis on cardiovascular magnetic resonance (CMR), assessed by late gadolinium enhancement (LGE) and parametric mapping, is an established predictor of adverse events in cardiomyopathy. We assessed whether artificial intelligence (AI) quantification of fibrosis adds independent prognostic value. METHODS: We searched six databases, a clinical-trials register, and a preprint server from inception to 13 June 2026. Eligible studies used AI to generate a fibrosis marker in adults with ischemic or nonischemic cardiomyopathy, with covariate-adjusted outcomes over ≥12 months. Risk of bias was assessed using PROBAST, PROBAST+AI, and QUIPS. Fewer than three comparable studies precluded meta-analysis; certainty was rated using GRADE. RESULTS: Of 448 records (381 after de-duplication), 18 full texts were reviewed and two included, one peer-reviewed and one preprint. In an ischemic-cardiomyopathy registry (Ghanbari et al.; n = 216 analytic, 26 events), AI-derived dense LGE scar predicted arrhythmic events (univariable hazard ratio [HR] 2.35, 95% CI 1.33-4.15), and AI-derived but not manual scar improved discrimination beyond guideline criteria (area under the curve 0.63 to 0.68; p = 0.02). In a nonischemic dilated-cardiomyopathy preprint (Kim et al.; n = 347, 119 events), automated extracellular volume ≥30% predicted cardiovascular death or heart-failure hospitalization (adjusted HR 2.00, 95% CI 1.32-3.03). Both were at high risk of bias, with data-derived thresholds and no external validation. CONCLUSIONS: Across only two studies, AI-derived fibrosis was independently associated with adverse cardiovascular events, but its added value over manual quantification remains unproven. Certainty was very low. The evidence base is sparse and not yet ready for clinical use.

Humans↗

PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗

PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗