Medical errors and patient safety: Strategies for reducing errors using artificial intelligence

Abstract

Background: Medical errors remain a significant challenge in healthcare, contributing to adverse patient outcomes, increased costs, and extended hospitalizations. These errors encompass diagnostic inaccuracies, medication mistakes, surgical errors, and communication breakdowns. The global prevalence of medical errors underscores the urgent need for effective strategies to enhance patient safety. Aim: This article explores the role of Artificial Intelligence (AI) in reducing medical errors and improving patient safety. It aims to evaluate how AI technologies can mitigate various types of medical errors, and the challenges associated with their implementation. Methods: The study reviews current literature on AI applications in healthcare, focusing on diagnostic support, medication safety, surgical precision, and patient monitoring. It analyzes the effectiveness of AI-driven systems in reducing errors across different medical disciplines and examines the integration challenges, including ethical and regulatory concerns. Results: AI technologies, including machine learning algorithms and decision support systems, have demonstrated significant potential in enhancing diagnostic accuracy, preventing medication errors, and improving surgical outcomes. AI-driven systems have shown promising results in real-time patient monitoring, early detection of adverse events, and optimizing healthcare management. However, challenges related to data privacy, algorithm transparency, and integration into clinical workflows persist.

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