AIMC Topic: Adult

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Representation of intensivists' race/ethnicity, sex, and age by artificial intelligence: a cross-sectional study of two text-to-image models.

Critical care (London, England)
BACKGROUND: Integrating artificial intelligence (AI) into intensive care practices can enhance patient care by providing real-time predictions and aiding clinical decisions. However, biases in AI models can undermine diversity, equity, and inclusion ...

Predicting malignancy in breast lesions: enhancing accuracy with fine-tuned convolutional neural network models.

BMC medical imaging
BACKGROUND: This study aims to explore the accuracy of Convolutional Neural Network (CNN) models in predicting malignancy in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging (DCE-BMRI).

A New Research Model for Artificial Intelligence-Based Well-Being Chatbot Engagement: Survey Study.

JMIR human factors
BACKGROUND: Artificial intelligence (AI)-based chatbots have emerged as potential tools to assist individuals in reducing anxiety and supporting well-being.

Evaluating the effectiveness of AI-powered UrologiQ's in accurately measuring kidney stone volume in urolithiasis patients.

Urolithiasis
Kidney stones and urolithiasis are kidney diseases that have a significant impact on health and well-being, and their incidence is increasing annually owing to factors such as age, sex, ethnicity, and geographical location. Accurate identification an...

Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

PLoS computational biology
To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) ...

Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches.

Frontiers in endocrinology
BACKGROUND: Type 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the ...

Explainable machine learning for early prediction of sepsis in traumatic brain injury: A discovery and validation study.

PloS one
BACKGROUND: People with traumatic brain injury (TBI) are at high risk for infection and sepsis. The aim of the study was to develop and validate an explainable machine learning(ML) model based on clinical features for early prediction of the risk of ...

Grade prediction of lesions in cerebral white matter using a convolutional neural network.

PloS one
We established a diagnostic method for cerebral white matter lesions using MRI images and examined the relationship between the MRI images and the medical checkup data. There were approximately 25 MRI images for each patient's head, from the top of t...

Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.

PloS one
BACKGROUND AND PURPOSE: Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality an...

Acoustical features as knee health biomarkers: A critical analysis.

Artificial intelligence in medicine
Acoustical knee health assessment has long promised an alternative to clinically available medical imaging tools, but this modality has yet to be adopted in medical practice. The field is currently led by machine learning models processing acoustical...