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[Atrial fibrillation screening-reading the P-wave]

Background Atrial fibrillation (AF) is frequently intermittent and may therefore remain undetected on a short resting electrocardiogram (ECG). At the same time, a sinus-rhythm ECG contains information on the underlying atrial substrate. Objective To review conventional and artificial intelligence (AI)-based ECG markers of an atrial risk phenotype and their potential role in targeted AF screening. Materials and methods Narrative appraisal of current consensus and guideline documents and of clinical, electrophysio…

Die Innere Medizin

Evidence-backed gain

Serum albumin is associated with immune-related hypothyroidism and treatment continuity in non-small cell lung cancer

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Generative AI versus physicians in diagnostic radiology: a systematic review and meta-analysis

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Explainable machine learning for identifying mild cognitive impairment in older adults with chronic diseases: Model development and temporal validation

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Rule-Based Versus Generative Extraction of Psychological Symptoms From Forensic Medical Certificates: A Feasibility Study in the French ORFeAD Network

Forensic medical certificates for victims of interpersonal violence describe psychological symptoms in unstructured prose, rarely explicit and requiring inference. To assess the feasibility of two extraction pipelines-rule-based and a locally served generative large language model-in a French multicentric forensic network, and to identify variables usable without human verification. 110 randomly selected 2021 certificates from 12 ORFeAD units were processed by both pipelines: rule-based (segmentation, lexicons,…

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Advancing Ultrasound Beamforming With Deep Learning: A Comprehensive Review of Methods, Datasets, Benchmarks, and Computational Challenges

Ultrasound imaging is a widely used diagnostic tool, and beamforming techniques are integral to its performance. Traditional methods such as delay-and-sum (DAS) and delay-multiply-and sum (DMAS) have limitations in terms of image quality and computational efficiency. Recently, deep learning approaches have shown significant promise in improving ultrasound beamforming, offering enhanced image resolution and quality, alongside potential for real-time processing. This review is aimed at providing a comprehensive ov…

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Applications and performance of imaging artificial intelligence in detecting and staging avascular necrosis of the femoral head: a systematic review and meta-analysis

Objective To evaluate the diagnostic accuracy of imaging artificial intelligence (AI) models for detecting avascular necrosis of the femoral head (AVNFH) and to examine whether diagnostic performance differs according to imaging modality, disease-stage focus, and validation strategy. Materials and methods In accordance with PRISMA-DTA, we conducted a systematic search in four main databases. Two independent reviewers evaluated the studies, extracted relevant data, and assessed the quality following the QUADAS-2…

Evidence-backed gain

Development and Validation of Human-AI Collaborative Workflow in SNOMED CT Mapping of Bilingual Clinical Text

Systematized Nomenclature of Medicine-Clinical Terminology (SNOMED CT) is the principal international standard for semantic interoperability of clinical information, but mapping free-text clinical narratives to SNOMED CT concepts remains labor-intensive. We developed a large language model agent system for mapping bilingual clinical text to SNOMED CT concepts and evaluated its effect on mapping accuracy and efficiency within a human-AI collaborative workflow. We designed a three-module agent system comprising tr…

Health

Generative artificial intelligence and commercial sexual exploitation of children: Implications for healthcare providers

Generative artificial intelligence (GAI) has created new avenues for the commercial sexual exploitation of children through the creation or manipulation of sexually explicit images using a child's likeness. Such images constitute a form of child sexual abuse material (CSAM), including when they are generated or manipulated without physical contact with the child. Because this abuse may occur without the child's awareness, it can complicate recognition, disclosure, and intervention while contributing to ongoing p…

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Artificial intelligence and digital technologies transforming neonatal respiratory assessment: a scoping review

Digital technologies and artificial intelligence (AI) have been increasingly incorporated into neonatal care, particularly in respiratory assessment. However, structured evidence regarding their clinical applications, performance, and limitations remains limited. This scoping review analyzed 35 studies published between 2019 and 2026 to map the use of digital technologies and AI in neonatal respiratory assessment. The findings showed that these approaches are mainly applied to predictive models, medical image an…

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Identifying incident medication-related osteonecrosis of the jaw and antiresorptive drug holidays using natural language processing on clinical notes in men and women prescribed bisphosphonates or denosumab for fracture prevention

Objective Medication-related osteonecrosis of the jaw (MRONJ) is a rare complication of antiresorptive therapy for osteoporosis, with risk potentially influenced by "drug holidays" around dentoalveolar procedures. MRONJ risk is higher in rheumatic diseases. Epidemiologic studies are limited by poor administrative code performance for MRONJ identification and lack of structured pharmacy data capturing drug holidays. We developed and validated a natural language processing (NLP) algorithm to classify MRONJ and det…

$Identifying incident medication-related osteonecrosis of the jaw and antiresorptive drug holidays using natural language processing on clinical notes in men and women prescribed bisphosphonates or denosumab for fracture prevention$

Health

Transforming healthcare delivery with conversational AI platforms

Healthcare communication faces unprecedented challenges as the healthcare workforce is increasingly faced with increased administrative burdens and reduced time with patients. Conversational agents powered by generative AI may offer a potential solution by collecting information, answering questions, documenting encounters, and supporting clinical decision-making through fluid, contextual dialogue. However, realizing their potential requires rigorous validation, careful implementation, and a strong commitment to…

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Predictive Value of the Albumin-Bilirubin Score for All-Cause Mortality in Individuals with Stroke: Machine Learning Prediction Models with SHAP-Based Interpretation

Background The albumin-bilirubin (ALBI) score, an objective liver function and prognostic indicator, has shown predictive value in various diseases, but its long-term prognostic significance in stroke survivors remains unclear. Objective To evaluate the ALBI score's predictive value for all-cause mortality (ACM) in individuals with stroke. Methods Data from the National Health and Nutrition Examination Survey (1999-2018) were analyzed. The Boruta algorithm selected key variables associated with ACM. Kaplan-Meier…

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Diagnostic accuracy of a DenseNet-121 deep learning algorithm for chest radiograph triage in health assessment applicants: a prospective shadow-mode validation study in Nepal

Objectives To evaluate the diagnostic accuracy of a publicly available DenseNet-121 convolutional neural network (TorchXRayVision) for triaging chest radiographs of health assessment applicants at a tertiary hospital in Nepal. Design Prospective, single-centre, shadow-mode diagnostic accuracy validation study. Reported in accordance with the Standards for Reporting of Diagnostic Accuracy Studies (STARD) 2015 checklist and STARD-Artificial Intelligence (AI)/Developmental and Exploratory Clinical Investigations of…

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Person-Based Progression in Acute Myeloid Leukemia Classification: A Multi-View Convolutional Neural Network Approach

Acute myeloid leukemia (AML) is a hematologic malignancy in which accurate subtype identification is important for treatment planning and prognosis. This study developed a deep learning approach for AML subtype classification using multiple single-cell images from each patient rather than conventional instance-based classification. An open-source dataset containing 81,214 single-cell images from 189 individuals was analyzed. The dataset included four genetically defined AML subtypes and a healthy control group.…