Artificial intelligence: promise and peril in achieving the quadruple aim in healthcare.

Front Artif Intell

Microsoft Research, Microsoft Corporation, Redmond, WA, United States.

Published: June 2024

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11220197PMC
http://dx.doi.org/10.3389/frai.2024.1430756DOI Listing

Publication Analysis

Top Keywords

artificial intelligence
4
intelligence promise
4
promise peril
4
peril achieving
4
achieving quadruple
4
quadruple aim
4
aim healthcare
4
artificial
1
promise
1
peril
1

Similar Publications

Aim: The purpose of this study is to compare the efficacy of an artificial intelligence (AI)-based care plan learning strategy with standard training techniques in order to determine how it affects nursing students' learning results in newborn resuscitation.

Methods: Seventy third-year nursing students from a state university in Türkiye participated in the study. They were split into two groups: the experimental group, which received care plans based on AI, and the control group, which received traditional instruction.

View Article and Find Full Text PDF

Vector-borne diseases pose a major worldwide health concern, impacting more than 1 billion people globally. Among various blood-feeding arthropods, mosquitoes stand out as the primary carriers of diseases significant in both medical and veterinary fields. Hence, comprehending their distinct role fulfilled by different mosquito types is crucial for efficiently addressing and enhancing control measures against mosquito-transmitted diseases.

View Article and Find Full Text PDF

Moving beyond word frequency based on tally counting: AI-generated familiarity estimates of words and phrases are an interesting additional index of language knowledge.

Behav Res Methods

December 2024

ETSI de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense, 30, 28040, Madrid, Spain.

This study investigates the potential of large language models (LLMs) to estimate the familiarity of words and multi-word expressions (MWEs). We validated LLM estimates for isolated words using existing human familiarity ratings and found strong correlations. LLM familiarity estimates performed even better in predicting lexical decision and naming performance in megastudies than the best available word frequency measures.

View Article and Find Full Text PDF

This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing between benign breast lesions (BBL) and malignant breast lesions (MBL). A total of 382 patients with breast lesions were included, comprising 183 benign lesions and 199 malignant lesions that were collected and confirmed through clinical pathology or biopsy. The enrolled patients were randomly allocated into two groups: a training cohort and an independent test cohort, maintaining a ratio of 7:3.

View Article and Find Full Text PDF

In this work, artificial neural network coupled with multi-objective genetic algorithm (ANN-NSGA-II) has been used to develop a model and optimize the conditions for the extracting of the Mentha longifolia (L.) L. plant.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!