Artificial Intelligence Advances for Medical Computer-Aided Diagnosis

• Format: eBook
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2024
• Subjects: Alzheimer’s disease; artificial intelligence; bank of features; breast cancer diagnoses; cardiovascular diseases; ChatGPT; chest X-ray; classification; CNN; conditional generative adversarial networks (CGANs); coronary angiograms; decision support system; deep learning; DenseNet169; diabetic retinopathy; disease detection; disease severity; early stopping; ensemble learning; evolutionary algorithm; feature selection; filter feature selection; GCE; genetic selection; heart diseases; hybrid spatial and k-space loss function; image classification; influenza; K-nearest neighbor; KARA-CXR; knee OA detection; LLM; machine learning; magnetic resonance imaging (MRI); medical diagnosis; medical image; medical image reconstruction; Medical specialties, branches of medicine; Medicine and Nursing; monkeypox disease; MSC; n/a; parallel imaging; Parkinson’s disease; Parkinson’s disease (PD); Pharmacology; Psychopharmacology; rehabilitation; self-adaptive; stenosis classification; stuttering detection; systematic review; XGBoost
• ISBN: 9783725816446; 3725816433; 9783725816439; 3725816441
• DOI: 10.3390/books978-3-7258-1643-9

This Special Issue, "Artificial Intelligence Advances for Medical Computer-Aided Diagnosis," explores the transformative role of AI and ML in modern diagnostics. Featuring contributions from leading researchers, this collection highlights innovative algorithms, models, and applications that enhance diagnostic accuracy, speed, and predictive power across various medical fields, including radiology, pathology, genomics, and personalized medicine. This issue includes 12 articles, spanning theoretical advancements, algorithm development, and practical implementations, showcasing the broad impact and future potential of AI-driven diagnostics. Each contribution offers unique insights into overcoming current challenges, improving clinical decision-making, and ultimately enhancing patient outcomes. This compilation serves as a valuable resource for researchers, clinicians, and technologists, fostering further innovation and collaboration in advancing medical diagnostics. Join us in exploring the latest breakthroughs and future directions in AI-driven healthcare, as we strive to improve diagnostic accuracy and efficiency for better patient care.