Emergency Medicine

[Regression analysis to calculate the time point of ROSC-A feasibility study].

BACKGROUND: A regression model to estimate the duration from the onset of resuscitation efforts to the return of spontaneous circulation (ROSC) could help improving both resuscitation care and the quality control of registries. This study aims to evaluate the prediction accuracy and to identify challenges for future model development. METHOD: Regression models based on M5P, random forest (RF) algo...

Deep Learning and Noninvasive Sensors for Detecting Physiological Dysregulation: A Scoping Review.

Early detection of pain, stress, or hemodynamic instability is key to preventing serious clinical events. In recent years, non-invasive sensors and deep learning algorithms have gained relevance as tools for accurate and continuous monitoring. To map and synthesize the scientific evidence on the use of non-invasive multimodal sensors combined with deep learning algorithms for the early detection o...

Incorporating artificial intelligence into imaging for surveillance and diagnosis of liver cancer: Innovations, challenges, and clinical translation.

Primary liver cancer, mainly hepatocellular carcinoma and intrahepatic cholangiocarcinoma, remains a leading cause of cancer mortality worldwide. Earl...

Health trajectories of patients with amyotrophic lateral sclerosis before and after initiation of non-invasive ventilation: a French nationwide database analysis.

BACKGROUND: Management of amyotrophic lateral sclerosis (ALS) is complicated by heterogeneous presentation and unpredictable disease course. This stud...

Transplatformer: translating toxicogenomic profiles between generations of platforms.

BACKGROUND: Transcriptomic profiling technologies have advanced the analysis of biological and toxicological responses. However, substantial differenc...

Artificial intelligence in resuscitation - transforming emergency care in the digital age.

AI-enabled forecasting of prehospital transfusion needs in patients with trauma: a multinational, registry-based, retrospective, machine learning development and validation study.

BACKGROUND: Trauma is a major global cause of morbidity and mortality, with haemorrhage representing a leading preventable cause of early death. Timel...

Hierarchy and hope: Exploring AI's role in medicine through a thematic analysis of online discourse.

The healthcare community remains divided on the benefits of artificial intelligence (AI) in medicine. In this qualitative study, we sought to better u...

Integrated omics and machine learning uncover the molecular basis of environmental toxicant 6PPD-Qinduced non-obstructive azoospermia.

BACKGROUND: N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone (6PPD-Q), a transformation product of tire rubber antioxidants, has been increa...

Evaluating Plastic Surgery Chatbot Performance: Insights into Medical Triage, Classification Accuracy, and Escalation Trends.

BACKGROUND: The integration of AI chatbots into plastic surgery websites is now standard, providing asynchronous, real-time engagement for patients. A...

Improving Clinical Decision-Making in Treating Airway Diseases With an Expert System Built Upon the Free AI Tool Google NotebookLM.

We used the free artificial intelligence (AI) tool Google NotebookLM, powered by the large language model Gemini 2.0, to construct a medical decision-...

Integrative transcriptomics, machine learning, and molecular docking reveal that nicotine exacerbates ischemic stroke: TLR4-dependent activation of NLRP3/caspase-1/GSDMD-mediated microglial pyroptosis.

OBJECTIVE: This study aimed to clarify the molecular mechanisms through which nicotine (Nic) aggravates ischemic stroke (IS), with an emphasis on infl...

Deep learning-based survival outcomes of REBOA vs resuscitative thoracotomy in trauma: a nationwide cohort study in South Korea.

Network toxicology and experimental validation reveal TDCPP as an emerging environmental risk factor for chronic kidney disease.

BACKGROUND: Tris(1,3-dichloro-2-propyl) phosphate (TDCPP) is a widely used organophosphorus flame retardant that has raised growing concern because it...

Prediction of intracerebral hemorrhage hematoma expansion: value of a novel deep learning system score.

OBJECTIVE: To develop and validate a deep learning system (DLS) model predicting hematoma expansion (HE) based on non-contrast (NC) CT and a score com...

Prospective Diagnostic Accuracy and Technical Feasibility of Artificial Intelligence-Assisted Rib Fracture Detection on Chest Radiographs: Observational Study.

BACKGROUND: Rib fractures are present in 10%-15% of thoracic trauma cases but are often missed on chest radiographs, delaying diagnosis and treatment....

Detection of Antithrombotic-Related Bleeding in Older Inpatients: Multicenter Retrospective Study Using Structured and Unstructured Electronic Health Record Data.

BACKGROUND: Bleeding complications are a major contributor to adverse drug events among older inpatients, particularly in those treated with antithrom...

Generating actionable insights to support point-of-care suicide risk decision-making in a safety-net healthcare system: a machine learning approach to predicting dynamic risk of intentional self-harm.

BACKGROUND: Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower ...

[Expert consensus on the application of artificial intelligence in stomatology].

In recent years, artificial intelligence (AI) has rapidly advanced in the field of oral medicine, with applications extending across disease screening...

From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology.

A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissu...