AIMC Topic: Decision Support Systems, Clinical

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Show and tell: A critical review on robustness and uncertainty for a more responsible medical AI.

International journal of medical informatics
This critical review explores two interrelated trends: the rapid increase in studies on machine learning (ML) applications within health informatics and the growing concerns about the reproducibility of these applications across different healthcare ...

Comparison of CT referral justification using clinical decision support and large language models in a large European cohort.

European radiology
BACKGROUND: Ensuring appropriate use of CT scans is critical for patient safety and resource optimization. Decision support tools and artificial intelligence (AI), such as large language models (LLMs), have the potential to improve CT referral justif...

When Machines Decide: Exploring How Trust in AI Shapes the Relationship Between Clinical Decision Support Systems and Nurses' Decision Regret: A Cross-Sectional Study.

Nursing in critical care
BACKGROUND: Artificial intelligence (AI)-based Clinical Decision Support Systems (AI-CDSS) are increasingly implemented in intensive care settings to support nurses in complex, time-sensitive decisions, aiming to improve accuracy, efficiency and pati...

Combining structural equation modeling analysis with machine learning for early malignancy detection in Bethesda Category III thyroid nodules.

Artificial intelligence in medicine
Atypia of Undetermined Significance (AUS), classified as Category III in the Bethesda Thyroid Cytopathology Reporting System, presents significant diagnostic challenges for clinicians. This study aims to develop a clinical decision support system tha...

Participatory Design for AI-Embedded Artifacts: The Case of ECEB App Design to Fostering Ownership.

Studies in health technology and informatics
AI-embedded artifacts in healthcare settings often perpetuate neocolonialism when designed and implemented without meaningful local participation, particularly in low- and middle-income countries. This paper examines how participatory design (PD) met...

Understanding Clinicians' Usage Patterns of the CONCERN Early Warning System: Insights from a Multi-Site Pragmatic Cluster Randomized Controlled Trial.

Studies in health technology and informatics
The CONCERN Early Warning System (EWS) uses artificial intelligence (AI) to analyze nursing documentation patterns, predicting hospitalized patients' risk of clinical deterioration. It generates real-time risk scores displayed on the electronic healt...

A Computational Framework for Tailored Preventive Care Recommendations Using Electronic Health Records.

Studies in health technology and informatics
Most healthcare systems worldwide are designed to be reactive. According to the U.S. Centers for Disease Control and Prevention (CDC), 90% of the nation's $3.3 trillion annual healthcare expenditures are attributed to individuals with chronic and men...

Role of AI in Clinical Decision-Making: An Analysis of FDA Medical Device Approvals.

Studies in health technology and informatics
The U.S. Food and Drug Administration (FDA) plays an important role in ensuring safety and effectiveness of AI/ML-enabled devices through its regulatory processes. In recent years, there has been an increase in the number of these devices cleared by ...

Predicting Antidepressant Deprescription with Machine Learning Using Administrative Data.

Studies in health technology and informatics
The high prevalence of failed antidepressant deprescription attempts makes it difficult for clinicians to identify suitable candidates for discontinuation. In this study, we use the Pharmaceutical Benefits Scheme (PBS) dataset, which contains rich lo...