From Joint Cognitive Systems to Human–AI Joint Cognitive Systems: A Theory Critique and Application to Obstetric Anesthesia Risk Assessment

Abstract

This article examines the joint cognitive systems framework as a foundation for understanding human–machine collaboration. Using Meleis’s theory evaluation model, we describe its origins, principles, and applications and then critique its strengths and limitations. Findings highlight challenges in trust, workload, and integration that limit effective use in health care. Application to obstetric anesthesia demonstrates how combining provider expertise with machine learning insights may improve maternal risk assessment. We propose the modernized extension, human–AI joint cognitive systems, to incorporate ethics and sociocultural factors, supporting safer and more effective clinical decision making.