Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 1,161 to 1,170 of 162,565 articles

Artificial intelligence fully automated analysis of handheld echocardiography in real-world patients with suspected heart failure.

European journal of heart failure
AIMS: Echocardiography is a rate-limiting step in the timely diagnosis of heart failure (HF). Automated reporting of echocardiograms has the potential to streamline workflow. The aim of this study was to test the diagnostic accuracy of fully automate... read more 

How Linguistics Learned to Stop Worrying and Love the Language Models.

The Behavioral and brain sciences
Language models can produce fluent, grammatical text. Nonetheless, some maintain that language models don't really learn language and also that, even if they did, that would not be informative for the study of human learning and processing. On the ot... read more 

Advances in postmortem interval estimation: A systematic review of machine learning and metabolomics across various tissue types.

Forensic science, medicine, and pathology
BACKGROUND: Traditional postmortem interval (PMI) estimation methods rely on observable changes such as rigor mortis, livor mortis, and algor mortis but are often affected by environmental factors. Metabolomics, combined with techniques like nuclear ... read more 

Fast identification of influenza using label-free SERS combined with machine learning algorithms clinical nasal swab samples.

Analytical methods : advancing methods and applications
Influenza virus outbreaks, which have become more frequent in recent years, have attracted global attention. Reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA), as the "gold standard" methods for vi... read more 

Ethical decision-making for AI in mental health: the Integrated Ethical Approach for Computational Psychiatry (IEACP) framework.

Psychological medicine
The integration of computational methods into psychiatry presents profound ethical challenges that extend beyond existing guidelines for AI and healthcare. While precision medicine and digital mental health tools offer transformative potential, they ... read more 

Deep Learning to Differentiate Parkinsonian Syndromes Using Multimodal Magnetic Resonance Imaging: A Proof-of-Concept Study.

Movement disorders : official journal of the Movement Disorder Society
BACKGROUND: The differentiation between multiple system atrophy (MSA) and Parkinson's disease (PD) based on clinical diagnostic criteria can be challenging, especially at an early stage. Leveraging deep learning methods and magnetic resonance imaging... read more 

Malignancy classification of thyroid incidentalomas using 18F-fluorodeoxy-d-glucose PET/computed tomography-derived radiomics.

Nuclear medicine communications
BACKGROUND: Thyroid incidentalomas (TIs) are incidental thyroid lesions detected on fluorodeoxy-d-glucose (18F-FDG) PET/computed tomography (PET/CT) scans. This study aims to investigate the role of noninvasive PET/CT-derived radiomic features in cha... read more 

Optimizing machine learning model selection for landslide susceptibility mapping: analysis of similar performance metrics and the critical role of multi-criteria evaluation.

Environmental science and pollution research international
Landslide susceptibility mapping has become an essential task to ensure economic and social sustainability. The use of machine learning algorithms has seen a wide range of applications and demonstrated high performance. However, researchers often fac... read more 

Decoding student cognitive abilities: a comparative study of explainable AI algorithms in educational data mining.

Scientific reports
Exploring students' cognitive abilities has long been an important topic in education. This study employs data-driven artificial intelligence (AI) models supported by explainability algorithms and PSM causal inference to investigate the factors influ... read more 

Mycophenolate mofetil-induced colitis versus colonic graft-versus-host disease: a comparative histologic study with artificial intelligence model development.

Histopathology
AIM: The aim of this study was to compare the histopathologic features of MMF-induced colitis and colonic GVHD and develop a digital tool using deep learning convolutional neural networks (CNNs) to semi-automate the quantification of eosinophils. read more