Artificial Intelligence in Clinical Decision Support: A Narrative Review of Opportunities and Concerns

Abstract

The purpose of this study is to critically explore the role and effectiveness of artificial intelligence (AI) in clinical decision support systems (CDSS) and its potential to improve diagnostic accuracy, treatment planning, and patient outcomes. As healthcare systems face growing demands, AI is seen as a promising tool to support clinicians in making timely, evidence-based decisions. This review synthesizes existing research from clinical trials, machine learning evaluations, and healthcare databases to analyze how AI technologies are currently embodied in CDSS. Key methods include identifying advantages and disadvantages in the analysis of medical decision making, focusing on machine learning models, and predictive analytics. Results indicate that AI-enhanced CDSS can improve diagnostic accuracy by up to 20% in certain fields such as radiology and dermatology. AI also helps with reducing medication errors. However, results also reveal limitations such as machine learning related algorithmic bias, lack of transparency (“black box” models), and clinician trust. To elaborate on black box models, clinicians can observe input-output correlations without insight into the internal decision logic. Addressing ethical concerns, ensuring diverse data representation, and involving clinicians in the design process are crucial for maximizing benefits. Future research should focus on improving validating AI systems in diverse clinical settings.

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