Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

T Shaik, X Tao, N Higgins, L Li… - … : Data Mining and …, 2023 - Wiley Online Library
The adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient
monitoring (RPM) is one of the common healthcare applications that assist doctors to …

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

AA Armoundas, SM Narayan, DK Arnett… - Circulation, 2024 - Am Heart Assoc
A major focus of academia, industry, and global governmental agencies is to develop and
apply artificial intelligence and other advanced analytical tools to transform health care …

Increasing acceptance of medical AI: the role of medical staff participation in AI development

W Huo, X Yuan, X Li, W Luo, J Xie, B Shi - International journal of medical …, 2023 - Elsevier
Background Medical artificial intelligence (AI) in varying degrees has exerted significant
influence on many medical fields, especially in the midst of the COVID-19 pandemic …

[HTML][HTML] Must-have qualities of clinical research on artificial intelligence and machine learning

B Koçak, R Cuocolo, DP Dos Santos… - Balkan Medical …, 2023 - ncbi.nlm.nih.gov
In the field of computer science, known as artificial intelligence, algorithms imitate reasoning
tasks that are typically performed by humans. The techniques that allow machines to learn …

Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review

YKJ Wan, MC Wright, MM McFarland… - Journal of the …, 2024 - academic.oup.com
Objective Surveillance algorithms that predict patient decompensation are increasingly
integrated with clinical workflows to help identify patients at risk of in-hospital deterioration …

Nurse practitioners' involvement and experience with AI-based health technologies: a systematic review

L Raymond, A Castonguay, O Doyon, G Paré - Applied Nursing Research, 2022 - Elsevier
Background Artificial intelligence (AI) is emerging in healthcare in various forms, including AI-
based clinical decision support systems, machine learning, computer vision, natural …

Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions

M Ennab, H Mcheick - Frontiers in Robotics and AI, 2024 - frontiersin.org
Artificial Intelligence (AI) has demonstrated exceptional performance in automating critical
healthcare tasks, such as diagnostic imaging analysis and predictive modeling, often …

Intelligent clinical decision support

MR Pinsky, A Dubrawski, G Clermont - Sensors, 2022 - mdpi.com
Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory
decompensation in the critically ill is difficult even in highly monitored patients in the …

Engaging multidisciplinary clinical users in the design of an artificial intelligence–powered graphical user interface for intensive care unit instability decision support

S Helman, MA Terry, T Pellathy… - Applied Clinical …, 2023 - thieme-connect.com
Background Critical instability forecast and treatment can be optimized by artificial
intelligence (AI)-enabled clinical decision support. It is important that the user-facing display …

An introduction to machine learning for speech-language pathologists: concepts, terminology, and emerging applications

C Cordella, MJ Marte, H Liu, S Kiran - Perspectives of the ASHA …, 2024 - pubs.asha.org
Purpose: The purpose of this article is to orient both clinicians and researchers to machine
learning (ML) approaches as applied to the field of speech-language pathology. We first …