The Role of Automation and Artificial Intelligence in Enhancing Laboratory Testing Quality and Efficiency in Low- and Middle-Income Countries: Review

Abstract

Background: The integration of automation and artificial intelligence (AI) into laboratory medicine has the potential to enhance diagnostic quality and operational efficiency, particularly in low- and middle-income countries (LMICs). These regions face significant challenges in healthcare delivery, including limited resources, inadequate infrastructure, and high disease burdens.

Methods: This review synthesizes current literature on the applications of AI and automation in laboratory settings, focusing on their impact on testing accuracy, efficiency, and patient outcomes. A comprehensive analysis of case studies and empirical data was conducted to evaluate the effectiveness of AI-driven technologies in addressing the unique challenges faced by LMICs.

Results: The findings indicate that AI technologies significantly improve diagnostic precision by automating routine tasks, enabling rapid data analysis, and enhancing decision-making processes. Case studies reveal that AI applications, such as machine learning algorithms and robotic systems, have led to substantial reductions in diagnostic errors and turnaround times. Furthermore, the implementation of AI has improved patient management through predictive analytics, facilitating early disease detection and personalized treatment strategies.

Conclusion: The adoption of automation and AI in laboratory medicine presents a transformative opportunity for LMICs to overcome existing healthcare disparities. While challenges such as data scarcity, infrastructural limitations, and ethical concerns remain, strategic investments in AI ecosystems can foster sustainable healthcare improvements. Future research should focus on developing contextually relevant AI frameworks to maximize the benefits of these technologies in resource-limited settings.