Beyond Single Systems: How Multi-Agent AI Is Reshaping Ethics in Radiology.

Area of Science:

• Artificial Intelligence in Medical Imaging
• Radiology Workflow Optimization
• Explainable AI (XAI)

Background:

• Radiology is shifting towards complex multi-agent AI systems for autonomous reasoning and workflow management.
• These systems extend beyond basic pattern recognition to handle intricate tasks like image analysis, report generation, and care coordination.
• While promising benefits, these advanced AI systems exacerbate the 'black box' problem, challenging traditional explainability methods.

Purpose of the Study:

• To examine the 'compound opacity' issue arising from multi-agent AI interactions in radiology.
• To analyze the autonomy-transparency paradox in radiological AI, where increased capability conflicts with interpretability needs.
• To propose frameworks for responsible implementation of agentic AI in radiology.

Main Methods:

• Analysis of emerging multi-agent radiological workflows.
• Examination of how agent interactions and distributed decision-making create layers of inscrutability.
• Review of traditional explainable AI limitations in complex, multi-step AI reasoning processes.

Main Results:

• Multi-agent AI systems in radiology create 'compound opacity' due to agent interactions and distributed decision-making.
• The increasing capability of radiological AI presents an autonomy-transparency paradox, challenging clinical trust and regulatory oversight.
• Existing explainable AI methods are insufficient for understanding the multi-step reasoning of these complex AI networks.

Conclusions:

• Agentic AI in radiology necessitates novel approaches to transparency and accountability.
• Frameworks for responsible implementation must balance diagnostic innovation with essential medical transparency.
• Addressing compound opacity is crucial for fostering trust and ensuring regulatory compliance in advanced AI-driven radiology.