AI Medical Compendium Topic:
Decision Support Systems, Clinical

Clear Filters Showing 571 to 580 of 696 articles

Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence.

Annals of internal medicine
Increasingly, interventions aimed at improving care are likely to use such technologies as machine learning and artificial intelligence. However, health care has been relatively late to adopt them. This article provides clinical examples in which mac...

Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance.

Medical physics
BACKGROUND: Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcar...

Concordance Study in Hepatectomy Recommendations Between Watson for Oncology and Clinical Practice for Patients with Hepatocellular Carcinoma in China.

World journal of surgery
BACKGROUND: With the improvement in diagnostic imaging, perioperative care and surgical technique, the indications and complexity of liver resections have developed. However, the surgical indications remain controversial especially for some complex o...

EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Predicting patient outcomes using healthcare/genomics data is an increasingly popular/important area. However, some diseases are rare and require data from multiple institutions to construct generalizable models. To address institutional d...

[Radiomics and artificial intelligence: new frontiers in medicine.].

Recenti progressi in medicina
Radiomics is a new frontier of medicine based on the extraction of quantitative data from radiological images which can not be seen by radiologist's naked eye and on the use of these data for the creation of clinical decision support systems. The lon...

Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support.

The journal of trauma and acute care surgery
INTRODUCTION: Admission computed tomography (CT) is a widely used diagnostic tool for patients with pelvic fractures. In this pilot study, we hypothesized that pelvic hematoma volumes derived using a rapid automated deep learning-based quantitative v...

Artificial Intelligence and Surgical Decision-making.

JAMA surgery
IMPORTANCE: Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that arti...

Artificial neural networks in neurorehabilitation: A scoping review.

NeuroRehabilitation
BACKGROUND: Advances in medical technology produce highly complex datasets in neurorehabilitation clinics and research laboratories. Artificial neural networks (ANNs) have been utilized to analyze big and complex datasets in various fields, but the u...

Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The study sought to describe the literature describing clinical reasoning ontology (CRO)-based clinical decision support systems (CDSSs) and identify and classify the medical knowledge and reasoning concepts and their properties within the...