Future-proofing medical assessment: evaluating collaborative intelligence in the age of artificial intelligence

Area of Science:

• Medical Education
• Artificial Intelligence
• Clinical Practice

Background:

• Conventional medical education struggles to assess competencies for human-AI collaboration.
• Existing AI frameworks often neglect the synergistic skills needed for effective teamwork.
• The integration of AI into healthcare necessitates new approaches to training and evaluation.

Purpose of the Study:

• To introduce and define Collaborative Intelligence (CI) as a human-AI team-centric construct.
• To outline the 5 core components of CI: critical appraisal, synthesis, judgment, ethics, and interaction.
• To propose a novel, competency-based assessment framework for CI.

Main Methods:

• Defined Collaborative Intelligence (CI) and its core components.
• Developed a 4-stage competency spiral (novice to expert) assessment model.
• Mapped various assessment methods (simulations, workplace assessments) to CI components across the spiral.

Main Results:

• CI is defined as the team's capability to appraise, integrate, and act on AI insights.
• A competency spiral model replaces traditional training stages for CI assessment.
• The proposed framework systematically evaluates CI across different expertise levels.

Conclusions:

• Reengineering medical assessment to measure CI is crucial for physician readiness.
• Effective CI measurement ensures physicians can harness AI while maintaining medical humanism.
• This approach is vital for safe, equitable healthcare in the AI era.