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AI-ICE Guided Pulsed Field Ablation of Atrial Fibrillation with Variable Loop Circular Catheter

Background Intracardiac echocardiography (ICE) facilitates left atrial (LA) reconstruction during atrial fibrillation (AF) ablation. The artificial intelligence-based CARTOSOUND FAM (AIFAM) module enables automated three-dimensional LA reconstruction without the need for a dedicated pre-ablation mapping catheter. While this workflow has been described previously in radiofrequency ablation, its application and outcome in pulsed field ablation (PFA) remains limited. Objective To evaluate the feasibility, safety, a…

Europace

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Intramuscular Fat Assessed by AI-Assisted Muscle Ultrasound: Association With Cardiometabolic Risk Factors and Diabetic Nephropathy in Diabetes Mellitus

Evidence-backed gain

Stacked Ensemble Deep Learning Models for Accurate Detection and Size Stratification of Periapical Lesions on Intraoral Radiographs

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Robotic Ultrasound Imaging: A Comprehensive Review of Historical Evolution, Current State-of-the-Art, and Future Perspectives

Evidence-backed gain

Adaptation of a fair individualized polysocial risk score for hospitalization risk prediction

Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally developed for patients with type 2 diabetes, to evaluate its generalizability in predicting 1-year hospitalization risk in a disease-agnostic adult cohort, with attention to fairness and explainability. Materials and methods The study utilized de-identified electronic health record data from a retrospective cohort of 17 857 adult patients at the University of Florida Health. The original iPsRS framework wa…

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Diagnosing melioidosis and tracking treatment outcomes using breath

Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagnosis and appropriate antimicrobial treatment are critical to reduce mortality, yet diagnosis is hindered by diverse clinical manifestations, mimicry with other diseases, and reliance on slow culture-based methods. Detecting volatile compounds offers a non-invasive approach for rapid infection detection. In this study, we aim to identify volatile compounds in patients' breath that can aid in diagnosing meli…

Evidence-backed gain

Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets

Classification is a core machine learning task, and ensemble voting methods are widely used to improve predictive accuracy in domains such as medical diagnosis, where class imbalance and non-linear decision boundaries are common. Conventional strategies: Majority Voting (MV), Weighted Voting (WV), and Soft Voting (SV) rely on static or classifier-level weighting schemes that fail to capture per-class differences in classifier reliability. Three dynamic, class-specific voting strategies are introduced: Highest Cl…

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AI-powered medicinal chemistry and translational drug development

Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into approved medicines remains slow, expensive, and prone to high attrition across the pipeline from target identification to clinical validation. Artificial intelligence (AI) is beginning to reshape this landscape by enabling large-scale integration, interpretation, and generation of chemical, biological, and clinical data for hypothesis generation, chemical space exploration, and iterative cycles of model-guided…

Health

The Importance of Artificial Intelligence in Nursing: A Fundamentals of Care Perspective

Aim To analyze the integration of Artificial Intelligence in nursing through the lens of the Fundamentals of Care framework. Design A discursive paper. Methods This discursive paper synthesizes current literature and theoretical perspectives to examine the Relationship, Integration and Context dimensions of the Fundamentals of Care framework in the era of Artificial Intelligence. Results Artificial Intelligence offers substantial benefits in optimizing workflow (Context) and clinical precision (Integration) thro…

Health

Assessing the Utility of Social Determinants of Health Data in Suicide Prediction Models

Objective This study seeks to explore the utility of social determinants of health (SDoH) variables in suicide prediction models. We aim to assess the impact of individual- and geographic-level SDoH factors on improving the performance of suicide prediction models and the identification of individuals at high risk for suicide. Methods A retrospective sample of 1214 deaths by suicide and 815,544 living patients was identified in the Maryland Suicide Data Warehouse (MSDW) and linked to census tract data through ge…

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Tissue-Agnostic Cellular Morphometric Biomarkers for Risk-Adapted Management Across Gastrointestinal Precancerous Lesions and Cancers

While precision oncology increasingly adopts tissue-agnostic paradigms, current strategies remain heavily reliant on molecular alterations, with limited relevance to early-stage cancers and precancerous lesion management. Here we present an unsupervised and interpretable artificial intelligence framework that defines tissue-agnostic cellular morphometric biomarkers (CMBs) capturing conserved tumor microenvironment (TME) architectures associated with cancer progression across gastrointestinal (GI) organs. Discove…

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Enhanced classification and identification of bacterial and viral microorganisms by integration of MALDI-TOF mass spectrometry with artificial intelligence

The accurate and rapid identification of bacterial pathogens is essential in clinical setups and medical biodefense. Matrix-Assisted Laser Desorption Ionization Time-Of-Flight (MALDI-TOF) mass spectrometry has emerged as a powerful tool for fast and reliable microbial identification. This study assesses the performance of eight Machine Learning (ML) and two Deep Learning (DL) models trained using 5-fold cross validation in classifying microorganisms in a series of experiments based on MALDI-TOF mass spectra (n =…

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Overcoming the opaque side of AI in healthcare: a lifecycle based approach

Introduction Transparency has emerged as a foundational condition for trustworthy Artificial Intelligence (AI) in healthcare. Despite its centrality, practical approaches to systematically operationalize transparency across the entire lifecycle of AI-enabled medical devices remain fragmented and insufficiently structured. This work addresses this gap by proposing a lifecycle-oriented operational approach to guide the consistent implementation and evaluation of transparency in AI-based medical technologies. Areas…

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Pathogenic Genetic Variants, Comorbid Autism and Adaptive Developmental Quotient as Independent Predictors of Intellectual Disability in Children With Global Developmental Delay: An Interpretable Machine Learning Model With Calibrated Risk Estimation

Background Global developmental delay (GDD) frequently precedes intellectual disability (ID), but no validated multivariable prognostic tool exists to support individualised counselling during the initial diagnostic work-up. Existing risk indicators are typically considered in isolation, and their joint contribution within an interpretable predictive framework remains uncertain. Methods We retrospectively analysed 2453 children diagnosed with GDD between January 2014 and December 2023 at a provincial tertiary ch…

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From accuracy to service: deciding what artificial intelligence outputs may do in veterinary diagnostic laboratories

Artificial intelligence (AI) tools are entering veterinary diagnostic laboratory service, but reported model accuracy does not determine what the laboratory staff should allow an output to do. This Commentary defines service entry as the point at which an AI output is allowed to influence case triage, interpretation, a draft report, or result release. Before that point, the laboratory staff should first decide whether the submitted specimen can support the question being asked. They should then document 7 decisi…