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CT-based deep learning for identifying sinonasal inverted papilloma: a single-center retrospective study
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CT-based deep learning for identifying sinonasal inverted papilloma: a single-center retrospective study

This study aimed to develop a CT-based deep learning model as a decision-support system for the classification of sinonasal inverted papilloma (IP), nasal polyps, and normal paranasal anatomy. A retrospective dataset of 871 patients who underwent paranasal CT imaging between 2020 and 2024 was analyzed. All diagnoses were confirmed histopathologically. Two representative CT slices per patient were selected at the patient level by an experienced radiologist blinded to final diagnosis. The dataset was split into 70…

European Archives of Oto-Rhino-Laryngology
Machine Learning-Driven Noninvasive Prediction of Liver Fibrosis in Unclassified Liver Disease
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Machine Learning-Driven Noninvasive Prediction of Liver Fibrosis in Unclassified Liver Disease

Chronic liver diseases (CLDs) frequently progress silently to advanced fibrosis or cirrhosis, yet existing predictive models have largely been developed for patients with clearly defined etiologies. Here, we asked whether a noninvasive machine-learning model based on routine clinical variables and liver stiffness measurement (LSM) could accurately identify significant liver fibrosis in patients with CLD of uncertain etiology, and whether such a model would add value beyond conventional fibrosis scores for clinic…

Mechanistic insight into Metabolic Activation Mechanism of Aniline Disinfection Byproducts catalyzed by P450 Enzymes from Distinct Species: N-hydroxylation Mechanism
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Mechanistic insight into Metabolic Activation Mechanism of Aniline Disinfection Byproducts catalyzed by P450 Enzymes from Distinct Species: N-hydroxylation Mechanism

Disinfection by-products (DBPs) are emerging environmental pollutants generated from reactions between disinfectants and precursor compounds in the water treatment, which pose a serious toxicity effect to the ecological environment and human health. Metabolism, especially by P450 enzymes, is responsible for both the toxification and detoxification of xenobiotics in humans and other organisms. Multidisciplinary theoretical approaches utilizing density functional theory (DFT), quantitative structure-activity relat…

Consistency and accuracy of different artificial intelligence models in evaluating longitudinal cracks and fractures of teeth
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Consistency and accuracy of different artificial intelligence models in evaluating longitudinal cracks and fractures of teeth

Large language models such as ChatGPT and Google Gemini have attracted increasing attention in dentistry due to their potential to support clinical decision-making; however, their reliability in responding to endodontic questions remains unclear. This study compared the accuracy, consistency, and temporal stability of three artificial intelligence models (ChatGPT-3.5, ChatGPT-4o, and Google Gemini) when answering dichotomous clinical questions on vertical root fractures and tooth cracks using expert responses as…

Engineering smart polymeric lipid nanoparticles for breast cancer: The convergence of AI and medical personalization to enhance efficacy and safety
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Engineering smart polymeric lipid nanoparticles for breast cancer: The convergence of AI and medical personalization to enhance efficacy and safety

Breast Cancer (BC) continues to be a predominant cause of cancer-related death globally, with clinical therapy impeded by off-target toxicity and limited therapeutic windows of traditional chemotherapeutics, especially across various molecular subtypes. This study critically assesses the integration of advanced nanocarrier engineering, Artificial Intelligence (AI), and personalized medicine in developing smart Polymeric-lipid nanoparticles (PLNs) for BC treatment. Preclinical data suggest that carefully designed…

Health

Unfolding new horizons: Machine learning applications for pediatric intussusception - A systematic review and meta-analysis

Background Pediatric intussusception is among the most common surgical emergencies in early childhood. Ultrasonography is the diagnostic standard but remains operator-dependent, and no validated machine learning (ML) tools exist for triage or prognostic stratification. We systematically evaluated ML models for diagnosis and prognosis of pediatric intussusception. Methods This PROSPERO-registered systematic review and meta-analysis (CRD420251073590) included studies evaluating ML models for pediatric intussuscept…

Unfolding new horizons: Machine learning applications for pediatric intussusception - A systematic review and meta-analysis
Health

Guideline-augmented prompting improves comparative preference and response consistency of large language model outputs for orthopaedic anaesthesia questions: A controlled prompting study

Purpose Large language models (LLMs) are increasingly used in clinical contexts; however, performance in complex perioperative decision-making remains uncertain. Orthopaedic anaesthesia presents a demanding test case due to comorbidity burden and guideline-dependent management. Whether successive LLM generations and guideline-augmented prompting improve clinical alignment, comparative performance and response consistency was evaluated in this study. Methods In this controlled prompting study, 34 orthopaedic anae…

Guideline-augmented prompting improves comparative preference and response consistency of large language model outputs for orthopaedic anaesthesia questions: A controlled prompting study
Health

A Multiparametric in vitro Strategy for Small Molecule Drug-Induced Liver Injury Risk Mitigation in Early Drug Development

Drug-induced liver injury (DILI) remains a major cause of preclinical and clinical attrition, reflecting persistent gaps in the predictive translation of preclinical safety data to human outcomes. To address these gaps, we established a refined multiparametric framework for early DILI risk prediction using a balanced reference set of 170 drugs with established clinical outcomes. Thirty physicochemical, mechanistic endpoints and exposure-related features were systematically evaluated to identify high-specificity…

A Multiparametric in vitro Strategy for Small Molecule Drug-Induced Liver Injury Risk Mitigation in Early Drug Development
Health

Using interpretable machine learning models to predict the occurrence of severe influenza in hospitalized children: a retrospective cohort study

Influenza, a prevalent disease, significantly threatens public health. Accurately predicting severe influenza occurrences is crucial for developing personalized prevention strategies and treatment plans. This study aimed to construct a highly interpretable model to assess the risk of severe influenza in hospitalized children, using the SHapley Additive exPlanation (SHAP) method to interpret the Random Forest (RF) model and identify risk factors for severe influenza. A retrospective cohort study was conducted, co…

Using interpretable machine learning models to predict the occurrence of severe influenza in hospitalized children: a retrospective cohort study
Health

Development and Nationwide Multicentre Evaluation of Guideline-Grounded Large Language Model Chatbots to Support Patient Self-Management and Education in Rheumatology

Patients with rheumatic diseases have persistent information needs that are not fully addressed in routine care. We developed and evaluated guideline-grounded, large language model (LLM) chatbots to support patient self-management and education in rheumatology.Ten disease-specific chatbots based on German guidelines were co-developed and deployed through 13 rheumatology centres and six patient organisations. Chatbot users rated responses and completed a questionnaire. User questions, feedback, and response chara…

Development and Nationwide Multicentre Evaluation of Guideline-Grounded Large Language Model Chatbots to Support Patient Self-Management and Education in Rheumatology
Health

Slice-Wise Quality Assessment of High b Value Breast DWI via Deep Learning-Based Artifact Detection

Diffusion-weighted imaging (DWI) can support lesion detection and characterization in breast MRI. Therefore, DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. However, especially high b value DWI can be prone to intensity artifacts that can affect diagnostic image assessment. To detect hyper- and hypointense artifacts on high b value DWI (b = 1500 s/mm2) using deep learning, employing either a binary classification (artifact prese…

Slice-Wise Quality Assessment of High b Value Breast DWI via Deep Learning-Based Artifact Detection
Health

ChatGPT Health and ChatGPT Plus in Urogynecology: A Blinded Comparison of Response Quality, Guideline Concordance, and Safety

Introduction and hypothesis ChatGPT Health is a health-focused conversational artificial intelligence (AI) environment, but its performance in patient-oriented urogynecology has not been directly compared with standard ChatGPT using both validated quality assessment and objective guideline-based criteria. We hypothesized that ChatGPT Health would provide higher patient-facing response quality while maintaining comparable guideline concordance and safety. Methods Twenty-four guideline-informed patient queries cov…

ChatGPT Health and ChatGPT Plus in Urogynecology: A Blinded Comparison of Response Quality, Guideline Concordance, and Safety