Artificial Intelligence Medical Compendium

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

Showing 521 to 530 of 200,021 articles

Integrating network toxicology, machine learning, and single-cell sequencing to reveal the FASN-mediated role of phenolic endocrine disruptors in water in promoting prostate cancer.

PloS one Jun 2, 2026
BACKGROUND: Phenolic endocrine-disrupting chemicals (EDCs) like nonylphenol (NP) and octylphenol (OP) are widespread water pollutants. Their estrogen-like properties are suspected contributors to prostate cancer, but their precise molecular mechanism... read more 

ProtAttn-QuadNet: An attention-based deep learning framework for protein-protein interaction prediction using ProtBERT embeddings.

PloS one Jun 2, 2026
Protein-protein interactions (PPIs) form the backbone of most cellular processes, governing signal transduction, gene regulation, and metabolic control. However, experimental approaches to identifying PPIs remain expensive, laborious, and often incom... read more 

Advanced persistent threat detection through multi-modal behavioral analysis.

PloS one Jun 2, 2026
Advanced Persistent Threats (APTs) represent sophisticated cyberattacks characterized by stealth, persistence, and evasion of traditional detection mechanisms. We observed that APT behaviors during lateral movement and data exfiltration share notable... read more 

Assessment of the accuracy of automated tooth segmentation using different orthodontic platforms and models with a large-scale clinical applicability.

European journal of orthodontics Jun 2, 2026
BACKGROUND/OBJECTIVES: Tooth segmentation remains the most time-consuming task during model preparation for digital orthodontic setup. This study aimed to assess the accuracy of AI-based tooth segmentation tools of four widely used orthodontic platfo... read more 

Tensor enhanced chest cancer classification via CNN and Vision Transformer models.

PloS one Jun 2, 2026
Lung diseases, particularly lung cancer, remain a leading cause of mortality worldwide, accounting for approximately 1.8 million deaths annually. Early and accurate diagnosis is critical for improving patient outcomes. This study also introduces a un... read more 

Transcriptome sequencing combined with experimental verification to explore potential key genes related to uric acid in diabetic retinopathy.

PloS one Jun 2, 2026
PURPOSE: Diabetic retinopathy (DR), a major microvascular complication of diabetes and leading global blindness cause, involves uric acid (UA) in its onset and progression. This study aimed to identify UA-related genes (UARGs) in DR and clarify their... read more 

The prognostic value of the early neutrophil-to-lymphocyte ratio for 28-day mortality in sepsis patients: A machine learning-based investigation of the MIMIC database.

PloS one Jun 2, 2026
BACKGROUND: The neutrophil-to-lymphocyte ratio (NLR) has shown inconsistent prognostic value in individuals with sepsis. This study aimed to clarify its ability to predict 28-day mortality via a machine learning-based analysis of a large ICU database... read more 

PepAnno: A structure-aware deep learning framework for bioactive peptide prediction, structural visualization, and physicochemical profiling.

PLoS computational biology Jun 2, 2026
Peptides are gaining prominence as therapeutic candidates due to their diverse physiological functions and structural simplicity. Although multiple computational tools exist for bioactive peptide prediction, many suffer from limitations such as non-i... read more 

A process-guided uncertainty-aware deep learning framework for reliable and interpretable industrial fault diagnosis.

PloS one Jun 2, 2026
Timely fault detection is essential for safety, product quality, and energy efficiency in advanced industrial processes. However, many existing fault diagnosis methods insufficiently exploit process structure and sensor reliability, which limits thei... read more 

Readiness Assessment for AI in Nursing Care Projects: Multimethods Study.

JMIR nursing Jun 2, 2026
BACKGROUND: Integrating artificial intelligence (AI) systems into nursing care often encounters obstacles stemming from unmet requirements and insufficient engagement with well-documented sociotechnical pitfalls. Readiness models offer a systematic w... read more 
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