AIMC Topic: Middle Aged
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Clinical Application of AI in Mammography: Insights from a Prospective Study.
Academic radiology
Jun 2, 2025
RATIONALE AND OBJECTIVES: This prospective study evaluated the performance of AI in a diagnostic clinic setting, comparing its effectiveness with radiologists of varying experience.
Machine Learning-Assisted Prediction of Persistent Incomplete Occlusion in Intracranial Aneurysms From Angiographic Parametric Imaging-Derived Features.
Academic radiology
Jun 2, 2025
RATIONALE AND OBJECTIVES: To develop machine-learning (ML) models incorporating angiographic parametric imaging (API)-derived parameters in predicting persistent incomplete occlusion of intracranial aneurysms (IAs) after flow diverter (FD) treatment.
A Machine Learning Trauma Triage Model for Critical Care Transport.
JAMA network open
Jun 2, 2025
IMPORTANCE: Under austere prehospital conditions, rapid classification of injured patients for intervention or transport is essential for providing lifesaving care. Discerning which patients need care most urgently further allows for optimal allocati...
Efficiency and Quality of Generative AI-Assisted Radiograph Reporting.
JAMA network open
Jun 2, 2025
IMPORTANCE: Diagnostic imaging interpretation involves distilling multimodal clinical information into text form, a task well-suited to augmentation by generative artificial intelligence (AI). However, to our knowledge, impacts of AI-based draft radi...
Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models.
JAMA network open
Jun 2, 2025
IMPORTANCE: Clinical artificial intelligence (AI) systems are susceptible to performance degradation due to data shifts, which can lead to erroneous predictions and potential patient harm. Proactively detecting and mitigating these shifts is crucial ...
Accuracy of Artificial Intelligence for Gatekeeping in Referrals to Specialized Care.
JAMA network open
Jun 2, 2025
IMPORTANCE: Integrating artificial intelligence (AI) technologies into gatekeeping holds significant potential, as it efficiently handles repetitive tasks and can process large amounts of information quickly.
Unsupervised learning-based quantitative analysis of CT intratumoral subregions predicts risk stratification of bladder cancer patients.
BMC medicine
Jun 2, 2025
BACKGROUND: Preoperative diagnosis of muscle invasion and American Joint Committee on Cancer (AJCC) stage plays a crucial role in guiding treatment strategies for bladder cancer (BCa). Utilizing quantitative analysis of tumor subregions via CT imagin...
Using machine learning to identify Parkinson's disease severity subtypes with multimodal data.
Journal of neuroengineering and rehabilitation
Jun 2, 2025
BACKGROUND: Classifying and predicting Parkinson's disease (PD) is challenging because of its diverse subtypes based on severity levels. Currently, identifying objective biomarkers associated with disease severity that can distinguish PD subtypes in ...
Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation.
BMC cardiovascular disorders
Jun 2, 2025
OBJECTIVE: Left atrial thrombus (LAT) poses a significant risk for stroke and other thromboembolic complication in patients with atrial fibrillation (AF). This study aimed to evaluate the incidence and predictors of LAT in patients with paroxysmal AF...
Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability.
BMC public health
Jun 2, 2025
BACKGROUND: Mild Cognitive Impairment (MCI) is a critical transitional stage between normal aging and Alzheimer's disease, and its early identification is essential for delaying disease progression.
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