Leveraging Digital tools and ai to Minimize Errors in Clinical Laboratories: A Systematic Review

Clinical laboratories are essential to modern healthcare, providing critical diagnostic data that guide patient treatment and management. However, laboratory errors, including misdiagnoses and incorrect test interpretations, can significantly impact patient safety and healthcare outcomes. The integration of digital tools and artificial intelligence (AI) has emerged as a transformative approach to minimizing these errors, enhancing diagnostic accuracy, optimizing workflows, and improving overall laboratory efficiency. This systematic review examines recent advancements in AI-driven diagnostic tools, laboratory automation, data analytics, and digital workflow optimization, highlighting their impact on reducing human errors in clinical laboratories. The review synthesizes findings from peer-reviewed studies published between 2016 and 2025, assessing the effectiveness of AI-based quality control mechanisms, automated laboratory information management systems (LIMS), and predictive analytics in ensuring data integrity and accuracy. While AI and automation present substantial benefits, challenges such as high implementation costs, data security concerns, and resistance to technological adoption remain significant barriers. Future directions include the integration of Internet of Things (IoT)-based monitoring, AI-driven personalized diagnostics, and enhanced regulatory frameworks to support widespread implementation. This review underscores the growing importance of AI and digital tools in revolutionizing laboratory operations, reducing diagnostic errors, and enhancing patient safety.