Artificial Intelligence Medical Compendium

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

Showing 11,391 to 11,400 of 209,934 articles

Hallmarks of epithelial-mesenchymal plasticity in cancer.

Molecular cancer
Cancer stem cells (CSCs) drive tumour initiation, progression, metastasis, and therapy resistance through their remarkable plasticity, enabling dynamic transitions between stem-like and differentiated states. A pivotal mechanism underlying this plast... read more 

Artificial intelligence for urodynamic studies: systematic review of methods, performance, and clinical applications.

BMC medical informatics and decision making
BACKGROUND AND OBJECTIVE: Interpretation of urodynamic studies (UDS) is essential for the assessment of lower urinary tract dysfunction but remains resource-intensive and highly operator-dependent. Artificial intelligence (AI) has increasingly been a... read more 

Standardizing antiviral response metrics for mono- and combination therapies in acute, chronic and latent viral infections.

Virology journal
The COVID-19 pandemic showed that heterogeneous antiviral assay designs, endpoints and reporting practices can obscure which candidate drugs and combinations are genuinely promising. As antiviral discovery expands from acute infections to chronic and... read more 

MicroRNAs in endometriosis: bioinformatics resources, machine learning strategies, and multi-omics perspectives.

Journal of translational medicine
BACKGROUND: Endometriosis is a heterogeneous gynecological disorder characterized by chronic pain, infertility, and substantial impairment of quality of life. Increasing evidence indicates that microRNAs (miRNAs) are key regulators of endometriosis p... read more 

A hybrid cluster-then-predict machine learning radiotherapy knowledge-based planning framework for similarity matching using holistic target-OAR constellation geometry.

Radiation oncology (London, England)
PURPOSE: Radiotherapy treatment planning is currently premised on individual clinical experience and use of many dose based optimization and knowledge-based planning (KBP) models. This study introduces and validates a novel KBP algorithm that matches... read more 

Predicting fire consequences with the transformer model based on multimodal feature fusion.

Journal of cheminformatics
Accurate prediction of fire consequences is fundamental to process safety management and quantitative risk assessment in the chemical process industries. Traditional empirical and computational fluid dynamics models often struggle to balance computat... read more 

Evaluating Injection Laryngoplasty Skills Using a Foundation Model: A Feasibility Study.

The Laryngoscope
OBJECTIVES: To evaluate the construct validity of a commercially available multimodal foundation model (Google Gemini 2.5 Pro) in assessing simulated injection laryngoplasty. METHODS: Thirty video recordings of simulated injection laryngoplasty proce... read more 

Machine learning-based preoperative classification of colorectal cancer stage using systemic inflammatory and nutritional biomarkers.

BMC gastroenterology
BACKGROUND: Accurate preoperative staging in colorectal cancer (CRC) is critical for determining the appropriate treatment strategy. This study evaluated whether early-stage (TNM I-II) and advanced-stage (TNM III-IV) CRC could be distinguished using ... read more 

Evaluating the performance of ChatGPT in responding to myotonic dystrophy type 1 patient inquiries: a specialist physician-based study.

BMC neurology
BACKGROUND: Advancements in artificial intelligence have led to the widespread use of large language models such as ChatGPT in healthcare communication. Myotonic dystrophy type 1, a chronic and multisystemic neuromuscular disorder, poses significant ... read more 

Leveraging the non-contrast CT component of PET/CT: an AI-driven delta-radiomics approach to monitor treatment response in metastatic breast cancer.

BMC medical imaging
PURPOSE: 18 F-FDG PET/CT is the standard modality for monitoring treatment response in metastatic breast cancer. This study aims to evaluate the predictive value of delta-radiomics derived solely from the low-dose, non-contrast CT component acquired ... read more