AIMC Topic: Humans

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Recommendations for using artificial intelligence in clinical flow cytometry.

Cytometry. Part B, Clinical cytometry
Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by hematopathologists with expert domain knowledge. Advances in artificial intelligence (AI) are trans...

Advances in pediatric perioperative care using artificial intelligence.

Current opinion in anaesthesiology
PURPOSE OF THIS REVIEW: This article explores how artificial intelligence (AI) can be used to evaluate risks in pediatric perioperative care. It will also describe potential future applications of AI, such as models for airway device selection, contr...

Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery.

British journal of anaesthesia
BACKGROUND: Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, def...

Automated curation of large-scale cancer histopathology image datasets using deep learning.

Histopathology
BACKGROUND: Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, espe...

Deep Learning based Retinal Vessel Caliber Measurement and the Association with Hypertension.

Current eye research
PURPOSE: To develop a highly efficient and fully automated method that measures retinal vessel caliber using digital retinal photographs and evaluate the association between retinal vessel caliber and hypertension.

Quantitative and Visual Analysis of Data Augmentation and Hyperparameter Optimization in Deep Learning-Based Segmentation of Low-Grade Glioma Tumors Using Grad-CAM.

Annals of biomedical engineering
This study executes a quantitative and visual investigation on the effectiveness of data augmentation and hyperparameter optimization on the accuracy of deep learning-based segmentation of LGG tumors. The study employed the MobileNetV2 and ResNet bac...

Large language models as tax attorneys: a case study in legal capabilities emergence.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Better understanding of Large Language Models' (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence and leveraging LLMs to identify inconsistencies in law. This paper explores...