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Decision Support Systems, Clinical

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Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology.

Cancer medicine
Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI-based decision-making tools in clinica...

The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Current Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we cond...

Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for cli...

Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts.

Machine Learning for Clinical Decision Support of Acute Streptococcal Pharyngitis: A Pilot Study.

The Israel Medical Association journal : IMAJ
BACKGROUND: Group A Streptococcus (GAS) is the predominant bacterial pathogen of pharyngitis in children. However, distinguishing GAS from viral pharyngitis is sometimes difficult. Unnecessary antibiotic use contributes to unwanted side effects, such...

Leveraging explainable artificial intelligence to optimize clinical decision support.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.

Integrating artificial intelligence techniques for advancements in colorectal cancer management: navigating past and predicting future direction.

JPMA. The Journal of the Pakistan Medical Association
Artificial Intelligence (AI) in the last few years has emerged as a valuable tool in managing colorectal cancer, revolutionizing its management at different stages. In early detection and diagnosis, AI leverages its prowess in imaging analysis, scrut...

Pharmaceutical Decision Support System Using Machine Learning to Analyze and Limit Drug-Related Problems in Hospitals.

Studies in health technology and informatics
The health product circuit corresponds to the chain of steps that a medicine goes through in hospital, from prescription to administration. The safety and regulation of all the stages of this circuit are major issues to ensure the safety and protect ...

How Well Do AI-Enabled Decision Support Systems Perform in Clinical Settings?

Studies in health technology and informatics
Real-world performance of machine learning (ML) models is crucial for safely and effectively embedding them into clinical decision support (CDS) systems. We examined evidence about the performance of contemporary ML-based CDS in clinical settings. A ...