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Artificial Intelligence

Image: Pericardial adipose tissue (PAT) segmentation: A visual comparison of the ground truth segmentations of the PAT (upper row), model’s predictions (middle row) and the subtraction of two masks. From left to the right, columns represent 2D axial slices from upper to lower heart levels and the rightmost column corresponds to the 3D representation of the masked region (Zahra Esmaeili et al. American Journal of Preventive Cardiology (2026). DOI: 10.1016/j.ajpc.2026.101549)

AI Analysis of Pericardial Fat Refines Long-Term Heart Disease Risk

An AI-based method quantifies fat around the heart (pericardial adipose tissue) to sharpen risk estimates without new imaging. More...
01 Apr 2026
Image: The approach analyzes commonly captured clinical variables to assign individualized risk for hepatocellular carcinoma (Photo credit: Adobe Stock)

Machine Learning Approach Enhances Liver Cancer Risk Stratification

A machine learning model estimates liver cancer risk using routine demographic, electronic health record, and blood test data. More...
30 Mar 2026
Image: connected devices offer new possibilities in the early detection of abnormalities or changes in brain health (Photo courtesy of 123RF)

New AI Approach Monitors Brain Health Using Passive Wearable Data

Data from smartwatches and smartphones can help monitor changes in brain health. More...
24 Mar 2026
Image: Study procedure flow chart. After patients with BSI were identified from microbiology databases, 27 patients\' characteristics were extracted from electronic health records. After the dimension of those variables was reduced with Uniform Manifold Approximation and Projection (UMAP), k-means++ was used to identify clusters. Three clinically distinct clusters were identified. Among those clusters, patients were divided into solid organ transplant (SOT) and non-SOT groups. Clinical characteristics and outcomes were evaluated (Masayuki Nigo et al., American Journal of Transplantation (2026). DOI: 10.1016/j.ajt.2025.10.019)

AI Tool Maps Early Risk Patterns in Bloodstream Infections

An AI model classifies bloodstream infection patients into clinically meaningful groups within the first 48 hours after diagnosis. More...
24 Mar 2026
Image: MIT PhD students Tiffany Yau (left) and Teya Bergamaschi who developed the deep learning model (Photo courtesy of Alex Ouyang/MIT Jameel Clinic)

AI Helps Predict Which Heart-Failure Patients Will Worsen Within a Year

An AI model uses ECG data to predict worsening heart failure up to a year in advance. More...
19 Mar 2026
Image: The Pre-MIRACLE2 tool enables early assessment of brain injury risk in cardiac arrest patients during ambulance care (Photo courtesy of 123RF)

Algorithm Allows Paramedics to Predict Brain Damage Risk After Cardiac Arrest

Paramedics can now predict brain injury risk after cardiac arrest before a patient’s hospital arrival. More...
17 Mar 2026
Image: The AI system detects acromegaly using images of the back of the hand and a clenched fist (Photo courtesy of Kobe University)

AI Model Identifies Rare Endocrine Disorder from Hand Images

A deep learning method can detect acromegaly by analyzing hand photographs. More...
10 Mar 2026
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 The Artificial Intelligence channel of HospiMedica keeps the reader informed about the latest news in AI-based clinical decision making, Medical knowledge engineering, Intelligent medical information systems and additional related fields.
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