Improving Job Performance by Artificial Intelligence (AI): An Integrated Framework for Biomedical Engineers

This study proposes an integrated framework that explains how competencies that encompass knowledge, skills, abilities, and attitudes (KSAA) influence job performance through Artificial Intelligence (AI) readiness among biomedical engineers. As AI continues to transform healthcare operations, biomedical engineers must demonstrate not only technical competence but also readiness to effectively adopt and use AI-driven tools. Drawing on human capital theory (Becker, 1993), and the capability-motivation-opportunity (AMO) model Appelbaum et al., (2000), and the KSAA framework, this study conceptualizes AI readiness as a mediating mechanism that strengthens the relationship between competency (independent variable) and job performance (dependent variable). This framework suggests that while competency provides a foundational capability for performance, AI readiness amplifies its impact by enhancing decision-making accuracy, innovation, and operational efficiency in AI-driven healthcare settings. These findings offer theoretical and practical implications whereby universities can embed AI readiness components into engineering curricula, and healthcare organizations can integrate AI competency assessments into professional development systems to prepare biomedical engineers for the challenges of Industry 4.0.