AI Medical Compendium Journal:
Med (New York, N.Y.)

Showing 1 to 10 of 16 articles

Accelerating drug discovery, development, and clinical trials by artificial intelligence.

Med (New York, N.Y.)
Artificial intelligence (AI) has profoundly advanced the field of biomedical research, which also demonstrates transformative capacity for innovation in drug development. This paper aims to deliver a comprehensive analysis of the progress in AI-assis...

Large-scale pretrained frame generative model enables real-time low-dose DSA imaging: An AI system development and multi-center validation study.

Med (New York, N.Y.)
BACKGROUND: Digital subtraction angiography (DSA) devices are commonly used in numerous interventional procedures across various parts of the body, necessitating multiple scans per procedure, which results in significant radiation exposure for both d...

Unraveling the physiological and psychosocial signatures of pain by machine learning.

Med (New York, N.Y.)
BACKGROUND: Pain is a complex subjective experience, strongly impacting health and quality of life. Despite many attempts to find effective solutions, present treatments are generic, often unsuccessful, and present significant side effects. Designing...

Artificial-intelligence-based risk prediction and mechanism discovery for atrial fibrillation using heart beat-to-beat intervals.

Med (New York, N.Y.)
BACKGROUND: Early diagnosis of atrial fibrillation (AF) is important for preventing stroke and other complications. Predicting AF risk in advance can improve early diagnostic efficiency. Deep learning hasĀ been used for disease risk prediction; howeve...

The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease.

Med (New York, N.Y.)
BACKGROUND: Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical proc...

Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes.

Med (New York, N.Y.)
BACKGROUND: Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size a...

Machine learning in clinical decision making.

Med (New York, N.Y.)
Machine learning is increasingly integrated into clinical practice, with applications ranging from pre-clinical data processing, bedside diagnosis assistance, patient stratification, treatment decision making, and early warning as part of primary and...

Refinement of the clinical variant interpretation framework by statistical evidence and machine learning.

Med (New York, N.Y.)
BACKGROUND: Although the American College of Medical Genetics andĀ Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines for variant interpretation are used widely in clinical genetics, there is room for improvement of these knowledge-bas...

Bridging the gaps: Overcoming challenges of implementing AI in healthcare.

Med (New York, N.Y.)
Artificial intelligence (AI) in healthcare promises transformative advancements, from enhancing diagnostics to optimizing personalized treatments. Realizing its full potential, however, requires addressing key challenges, including explainability, bi...

Shaping the future of heart health.

Med (New York, N.Y.)
For World Heart Day on September 24, 2024, the World Heart Federation urges nations to endorse national strategies for enhancing cardiovascular health. While advancements show promise in reducing atherosclerosis, addressing healthcare inequalities an...