Integrating AI in Clinical Biochemistry: A Qualitative Study of Laboratory Technicians’ Knowledge, Skills, and Challenges in Al Khums, Libya - Esmaeil B. Musa; Omran A. Attir; Mohamed Abdulsamad; Souha M. Amer; Salsabil M. Al-Alfaidy; Eman R. Kawan (African Journal of Advanced Pure and Applied Sciences, 2025)
This study aims to assess the existing knowledge and proficiency of laboratory technicians in using Artificial Intelligence (AI) tools to interpret laboratory data in clinical biochemistry laboratories in Al Khums, Libya. It seeks to identify gaps in understanding, evaluate technicians' educational preparedness, and examine the challenges and competencies required for the effective integration of AI into laboratory practices as well. This qualitative descriptive cross-sectional study adopts a descriptive-analytical approach, employing semi-structured interviews with a sample of 105 laboratory technicians working in both governmental and private clinical biochemistry laboratories. The interview framework explores key themes, including awareness of AI technologies, perceived benefits and challenges of AI adoption, necessary training and skill development, ethical considerations, and general attitudes toward AI integration in laboratory workflows. The results indicate varying levels of familiarity and confidence among laboratory technicians regarding AI applications in laboratory practice. While many participants demonstrated an awareness of AI and expressed openness to its adoption, substantial gaps in technical knowledge and hands-on experience were identified. The study underscores the necessity for enhanced training programs that encompass both the technical and ethical dimensions of AI. Additionally, it highlights the importance of interdisciplinary collaboration between laboratory professionals and AI developers to facilitate effective implementation.