AIMC Topic: Artificial Intelligence
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AI-driven innovations in smart multifunctional nanocarriers for drug and gene delivery: A mini-review.
Critical reviews in oncology/hematology
Mar 13, 2025
The convergence of artificial intelligence (AI) and nanomedicine has revolutionized the design of smart multifunctional nanocarriers (SMNs) for drug and gene delivery, offering unprecedented precision, efficiency, and personalization in therapeutic a...
Patellar tilt calculation utilizing artificial intelligence on CT knee imaging.
The Knee
Mar 13, 2025
BACKGROUND: In the diagnosis of patellar instability, three-dimensional (3D) imaging enables measurement of a wide range of metrics. However, measuring these metrics can be time-consuming and prone to error due to conducting 2D measurements on 3D obj...
Harnessing generative AI in exercise and sports science education: enhancing real-world learning and overcoming traditional barriers in data analysis.
Advances in physiology education
Mar 13, 2025
Generative AI (GenAI) offers transformative potential for exercise and sports science education, addressing traditional data analysis and visualization barriers while promoting real-world learning. This Perspectives article explores how integrating G...
Artificial Intelligence-Assisted Capsule Endoscopy Versus Conventional Capsule Endoscopy for Detection of Small Bowel Lesions: A Systematic Review and Meta-Analysis.
Journal of gastroenterology and hepatology
Mar 13, 2025
BACKGROUND: Capsule endoscopy (CE) is a valuable tool used in the diagnosis of small intestinal lesions. The study aims to systematically review the literature and provide a meta-analysis of the diagnostic accuracy, specificity, sensitivity, and nega...
Molecular Generation for CNS Drug Discovery and Design.
ACS chemical neuroscience
Mar 13, 2025
Computational drug design is a rapidly evolving field, especially the latest breakthroughs in generative artificial intelligence (GenAI) to create new compounds. However, the potential of GenAI to address the challenges in designing central nervous s...
Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review.
Journal of the American Heart Association
Mar 13, 2025
Electronic health records (EHR) have revolutionized cardiovascular disease (CVD) research by enabling comprehensive, large-scale, and dynamic data collection. Integrating EHR data with advanced analytical methods, including artificial intelligence (A...
Integration of artificial intelligence in radiology education: a requirements survey and recommendations from faculty radiologists, residents, and medical students.
BMC medical education
Mar 13, 2025
BACKGROUND: To investigate the perspectives and expectations of faculty radiologists, residents, and medical students regarding the integration of artificial intelligence (AI) in radiology education, a survey was conducted to collect their opinions a...
Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation.
Scientific reports
Mar 13, 2025
Periodontal inflammation is a chronic condition affecting the tissues surrounding teeth. Initiated by dental plaque, it triggers an immune response leading to tissue destruction. The AIM-2 inflammasome regulates this response, and understanding its p...
A multi model deep net with an explainable AI based framework for diabetic retinopathy segmentation and classification.
Scientific reports
Mar 13, 2025
Diabetic Retinopathy (DR) is a serious condition affecting diabetes people caused by hemorrhage in the light-sensitive retinal area. DR sufferers should receive urgent therapy to avoid vision loss. The intelligent medical diagnosis system for DR is e...
Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.
Scientific reports
Mar 13, 2025
Classifying medical images is essential in computer-aided diagnosis (CAD). Although the recent success of deep learning in the classification tasks has proven advantages over the traditional feature extraction techniques, it remains challenging due t...
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