B-161 Enhancing Clinical Laboratory Operations with AI: The Role of ChatGPT in Quality Control, Troubleshooting, and Workflow Optimization

Abstract Background Artificial intelligence (AI) is increasingly being integrated into healthcare systems to optimize efficiency and accuracy. Clinical laboratories, which rely on standardized workflows and quality control (QC) procedures to ensure reliable results, face challenges in troubleshooting, regulatory compliance, and staff training. While AI has shown promise in clinical decision-making, its use in this capacity is still being evaluated and will require extensive validation and regulatory oversight. However, AI presents an opportunity to streamline operational processes by providing real-time guidance on QC investigations, workflow management, and regulatory adherence. Additionally, AI-driven tools can aid in data interpretation, flagging anomalies in test results that may indicate instrument malfunctions or systemic issues, thus reducing human error and improving reliability. Methods ChatGPT was customized for clinical laboratory applications with a focus on QC troubleshooting and workflow optimization. The AI model was trained to assist in identifying common QC failures, analyzing trends in Levy-Jennings plots, and guiding corrective actions. Furthermore, the system was designed to integrate historical QC data, allowing for pattern recognition and predictive analytics to anticipate potential issues before they impact testing accuracy. Additionally, ChatGPT was integrated with regulatory guidelines to support compliance efforts, offering documentation recommendations and audit preparation assistance. A pilot study was conducted in a clinical laboratory setting, where laboratory staff utilized ChatGPT for troubleshooting and workflow-related queries. Results The implementation of ChatGPT demonstrated notable improvements in QC troubleshooting efficiency. Staff reported a significant reduction in troubleshooting time due to structured AI-guided workflows and immediate access to relevant QC insights. The AI*s ability to analyze Levy-Jennings plots and identify shifts and trends provided laboratory professionals with actionable recommendations for adjusting QC parameters, thus improving accuracy and reducing retesting requirements. Additionally, ChatGPT assisted in automating routine documentation, ensuring that corrective actions and compliance protocols were consistently followed and recorded. Users found the AI tool beneficial in streamlining regulatory compliance tasks, enhancing productivity, and minimizing errors associated with manual documentation. Feedback highlighted ChatGPT’s effectiveness in improving consistency in troubleshooting processes, reducing lab downtime, and serving as an educational tool for new staff members, ultimately strengthening overall laboratory performance. Conclusion This study highlights the potential of AI-driven solutions, such as ChatGPT, in enhancing clinical laboratory operations. By providing structured guidance in QC troubleshooting, workflow management, and regulatory compliance, ChatGPT contributes to increased efficiency and consistency in laboratory processes. Moreover, the AI’s predictive capabilities offer a proactive approach to QC management, allowing laboratories to address potential issues before they escalate. Future advancements may further refine AI models to support laboratory professionals in more complex decision-making scenarios, incorporating machine learning techniques to continuously improve AI performance. By leveraging AI in laboratory settings, institutions can optimize quality control, reduce errors, and ultimately improve patient outcomes through enhanced laboratory performance and efficiency. These findings support the integration of AI tools in clinical laboratory settings to optimize quality and operational workflows.