This study proposes an evaluation of the efficacy of machine learning algorithms in classifying chronic pain based on Italian nursing notes, contributing to the integration of artificial intelligence tools in healthcare within an Italian linguistic c...
International journal of surgery (London, England)
May 1, 2025
BACKGROUND: The majority of patients with hepatocellular carcinoma (HCC) miss the opportunity of radical resection, making immune check-point inhibitors (ICIs)-based conversion therapy a primary option. However, challenges persist in predicting respo...
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
May 1, 2025
This study investigated the added value of using maximum-intensity projection (MIP) images for fully automatic segmentation of lesions using deep learning (DL) in [F]FDG and [Ga]Ga-prostate-specific membrane antigen (PSMA) PET/CT scans. We used 489 ...
This study aimed to compare Generations X, Y, and Z in terms of anxiety and readiness levels regarding artificial intelligence and investigate the relationship between anxiety and readiness levels regarding artificial intelligence in midwives across ...
Electronic incident reporting is a key quality and a safety process for healthcare organizations that assists in evaluating performance and informing quality improvement initiatives. Although it is mandatory for high-severity incident reports to be i...
OBJECTIVES: Neurological emergencies pose significant challenges in medical care in resource-limited countries. Artificial intelligence (AI), particularly health chatbots, offers a promising solution. Rigorous validation is required to ensure safety ...
International journal of surgery (London, England)
May 1, 2025
BACKGROUND: High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-fr...
PURPOSE: Compare the impact of photon-counting detector computed tomography (PCD-CT) to conventional CT on an interstitial lung disease (ILD) quantitative machine learning (QML) model.
PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor ( EGFR ) and TP53 mutations and to assess the models' capacities t...
PURPOSE: To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).
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