Reconfiguring work: artificial intelligence, agentic AI, and the future of the radiology profession

Area of Science:

• Medical Imaging and Diagnostics
• Artificial Intelligence in Healthcare
• Radiology Workflow Optimization

Background:

• The integration of artificial intelligence (AI) is significantly altering the field of radiology.
• Emerging agentic AI systems are poised to further transform radiological tasks and responsibilities.
• Current discussions on AI's impact are limited to job displacement or task automation/augmentation.

Purpose of the Study:

• To introduce and advance a novel "reconfiguration" approach for understanding AI's impact on radiology.
• To shift the focus from job loss and task automation to the evolving nature of radiological work.
• To provide a framework for analyzing how AI integration reshapes tasks, responsibilities, and professional roles in radiology.

Main Methods:

• Conceptual framework development based on current AI research and trends.
• Analysis of how AI influences task connectivity, responsibility distribution, and professional role evolution.
• Exploration of clinical, educational, and organizational shifts in radiological practice due to AI.

Main Results:

• The "reconfiguration" approach offers a more comprehensive perspective on AI's impact than displacement or automation/augmentation models.
• AI integration necessitates a re-evaluation of how radiological tasks are connected and managed.
• Professional roles and responsibilities in radiology are expected to evolve significantly, not just be replaced or augmented.

Conclusions:

• The reconfiguration model provides a clearer lens for understanding and proactively shaping the future of work in radiology.
• Embracing reconfiguration allows for strategic adaptation to AI-driven changes in clinical, educational, and organizational domains.
• This approach is crucial for navigating the evolving landscape of AI in medical imaging and ensuring a future-ready radiology workforce.