AIMC Topic: Female

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Artificial Intelligence and Radiologist Burnout.

JAMA network open
IMPORTANCE: Understanding the association of artificial intelligence (AI) with physician burnout is crucial for fostering a collaborative interactive environment between physicians and AI.

Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model.

Sensors (Basel, Switzerland)
To enhance the classification accuracy of lower limb movements, a fusion recognition model integrating a surface electromyography (sEMG)-based convolutional neural network, transformer encoder, and long short-term memory network (CNN-Transformer-LSTM...

Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure.

JAMA network open
IMPORTANCE: Serial functional status assessments are critical to heart failure (HF) management but are often described narratively in documentation, limiting their use in quality improvement or patient selection for clinical trials.

Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models.

European journal of medical research
OBJECTIVES: Our research aims to construct machine learning prediction models to identify patients proned to recurrence after inverted papilloma (IP) surgery and guide their follow-up treatment.

Explainable fully automated CT scoring of interstitial lung disease for patients suspected of systemic sclerosis by cascaded regression neural networks and its comparison with experts.

Scientific reports
Visual scoring of interstitial lung disease in systemic sclerosis (SSc-ILD) from CT scans is laborious, subjective and time-consuming. This study aims to develop a deep learning framework to automate SSc-ILD scoring. The automated framework is a casc...

Prediction and clustering of Alzheimer's disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data.

Scientific reports
Early detection of Alzheimer's disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The ob...

Liquid saliva-based Raman spectroscopy device with on-board machine learning detects COVID-19 infection in real-time.

The Analyst
With greater population density, the likelihood of viral outbreaks achieving pandemic status is increasing. However, current viral screening techniques use specific reagents, and as viruses mutate, test accuracy decreases. Here, we present the first ...

BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning.

IEEE transactions on medical imaging
Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical ...

Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning.

Frontiers in cellular and infection microbiology
BACKGROUND: This study aimed to characterize the oral and gut microbiota in prediabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) patients while exploring the association between tongue manifestations and the oral-gut microbiota axis in d...

Integrating large language models in mental health practice: a qualitative descriptive study based on expert interviews.

Frontiers in public health
BACKGROUND: Progress in developing artificial intelligence (AI) products represented by large language models (LLMs) such as OpenAI's ChatGPT has sparked enthusiasm for their potential use in mental health practice. However, the perspectives on the i...