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

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Publicado en:Bioengineering vol. 12, no. 10 (2025), p. 1100-1122
Autor principal: Salehi, Sara
Otros Autores: Singh Yashbir, Habibi Parnian, Erickson, Bradley J
Publicado:
MDPI AG
Materias:
Image analysis
Agents (artificial intelligence)
Collaboration
Artificial intelligence
Pattern recognition
Decision making
Workflow
Reasoning
Image processing
Multiagent systems
Explainable artificial intelligence
Large language models
Radiology
Autonomy
Agentic artificial intelligence
Acceso en línea:Citation/Abstract
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Resumen:Radiology is undergoing a paradigm shift from traditional single-function AI systems to sophisticated multi-agent networks capable of autonomous reasoning, coordinated decision-making, and adaptive workflow management. These agentic AI systems move beyond simple pattern recognition to encompass complex radiological workflows including image analysis, report generation, clinical communication, and care coordination. While multi-agent radiological AI promises enhanced diagnostic accuracy, improved workflow efficiency, and reduced physician burden, it simultaneously amplifies the long-standing “black box” problem. Traditional explainable AI methods, which are adequate for understanding isolated diagnostic predictions, fail when applied to multi-step reasoning processes involving multiple specialized agents coordinating across imaging interpretation, clinical correlation, and treatment planning. This paper examines how agentic AI systems in radiology create “compound opacity” layers of inscrutability from agent interactions and distributed decision-making processes. We analyze the autonomy–transparency paradox specific to radiological practice, where increasing AI capability directly conflicts with interpretability requirements essential for clinical trust and regulatory oversight. Through examination of emerging multi-agent radiological workflows, we propose frameworks for responsible implementation that preserve both diagnostic innovation and the fundamental principles of medical transparency and accountability.
ISSN:2306-5354
DOI:10.3390/bioengineering12101100
Fuente:Engineering Database