Artificial Intelligence Medical Compendium

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

Showing 221 to 230 of 199,772 articles

Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.

Allergy
Artificial intelligence (AI) in environmental health science is revolutionizing data analysis and problem-solving approaches. These technologies facilitate the prediction of environmental exposures and disease outcomes and enable the identification o... read more 

Toward a Synthetic Data Revolution: Diffusion Model-Enhanced Hepatocellular Carcinoma Prediction in Steatotic Liver Disease.

Hepatology research : the official journal of the Japan Society of Hepatology
AIM: Steatotic liver disease (SLD) encompasses a heterogeneous spectrum with varying risks of hepatocellular carcinoma (HCC). Limited sample sizes limit the development of predictive models, particularly for rare outcomes. This study evaluated whethe... read more 

Ethical and Legal Challenges of Partially and Fully Autonomous AI in Healthcare: Reinterpreting Liability and Preserving Trust.

Bioethics
Artificial intelligence (AI) is increasingly embedded in healthcare in a variety of ways, ranging from semi-autonomous decision support systems to the various visions of completely autonomous clinical systems. This article explores the ethical and le... read more 

Epigenetic reprogramming of tissue-resident memory T cells in chronic inflammatory disorders and implications for targeted therapies.

Epigenomics
BACKGROUND: Tissue-resident memory T (TRM) cells play a role in causing long-term tissue injury in chronic inflammatory diseases via pathological epigenetic reprogramming. Nevertheless, the epigenetic processes that cause this malfunction have not be... read more 

Body-Donor-Derived Data in Medical Artificial Intelligence: From Foundational Resources to Trustworthy Applications.

Clinical anatomy (New York, N.Y.)
In recent years, artificial intelligence in medicine has evolved from single recognition tasks toward structural understanding, spatial reasoning, and clinical interpretability. High-quality anatomical data have become a key factor in further develop... read more 

Structural, Compositional, and Dielectric State Profiling in Label-Free Single-Cell Monitoring.

Small methods
Individual cells sense and transition between functional states, and the distribution of these states over time determines drug response, disease progression, and cell manufacturing outcomes. However, repeated measurement is difficult with label-base... read more 

Nutritional Characteristics of Foods With Addictive Potential: A Machine-Learning Approach.

American journal of public health
Objectives. To identify nutritional characteristics associated with the perceived addictive potential of commonly consumed foods in the US food supply, the majority of which are ultraprocessed foods (UPFs). Methods. In a demographically diverse sampl... read more 

Fluorescence spectroscopy and machine learning methods for detection of Alzheimer's disease from circulating white blood cells.

Journal of Alzheimer's disease : JAD
BackgroundAlzheimer's disease (AD) is the most common cause of dementia whose prevalence is projected to increase significantly in the coming decades. The recent advent of disease modifying therapies is a welcome development; however, it is also now ... read more 

Connected-speech digital biomarkers for monitoring transcranial pulse stimulation in Alzheimer's disease: A pilot study.

Journal of Alzheimer's disease : JAD
BackgroundAlzheimer's disease (AD) lacks effective disease-modifying therapies and scalable, ecologically valid biomarkers to monitor treatment response. Transcranial pulse stimulation (TPS) is an emerging non-invasive neuromodulation technique with ... read more