Artificial Intelligence Medical Compendium

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

Showing 59,561 to 59,570 of 228,014 articles

Multidisciplinary artificial intelligence systems versus single-model approaches for the diagnosis and management of ileus and volvulus.

BMC gastroenterology
BACKGROUND AND AIMS: The accurate and timely diagnosis of ileus versus volvulus is essential in emergency care, as treatment choices directly influence patient outcomes. In this study, the diagnostic accuracy and adherence to guidelines of multidisci... read more 

Identification of endometrial cancer biomarkers using weighted gene coexpression network analysis and machine learning.

BMC cancer
BACKGROUND: Endometrial carcinoma (UCEC) exhibits a rising incidence in China, imposing a substantial burden on both women and society. Identifying biomarkers for UCEC is critical for precise diagnosis and treatment. METHODS: RNA-seq data for UCEC an... read more 

Digital horizons: Exploring the future of AI-enabled gastroenterology in the Kingdom of Saudi Arabia.

Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association
Artificial intelligence is rapidly reshaping gastroenterology through demonstrable gains in diagnostic precision, procedural quality, and operational efficiency. AI-assisted colonoscopy has consistently shown absolute improvements of 5%-10% in adenom... read more 

Data-Driven Design and Fabrication of Heat-Resistant, Ultrastrong, Lightweight Aluminum-Based Entropy Alloy by Additive Manufacturing.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Additive manufacturing (AM) of heat-resistant high-strength aluminum (Al) alloys for load-bearing components faces a fundamental dichotomy: traditional high-strength compositions suffer from hot cracking, while printable alloys lack sufficient high-t... read more 

Predicting S1 TDDFT Energies from ZINDO Calculations Using Message-Passing ΔML with Electronically Informed Descriptors.

Journal of chemical theory and computation
We present a machine learning approach (ΔML) capable of enhancing the accuracy of semiempirical excited-state energy calculations to a level close to that of Time-Dependent Density Functional Theory (TDDFT). Using a data set of 7600 organic π-conjuga... read more