Ethical issues in multi-agent AI systems for healthcare: a narrative review

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Abstract
Introduction: Multi-agent AI systems are believed to bring significant improvements in digital health, but it also brings new and more serious ethical issues. Such systems distribute the decision-making process among multiple interacting agents, and this decentralized decision-making system has raised ethical concerns in the medical field. On the one hand, it continues the ethical issues of traditional AI tools; on the other hand, the interaction processes within complex systems have also brought about new dilemmas. This narrative review aims to synthesize the ethical issues related to multi-agent AI systems in healthcare presented and explore the corresponding mitigation strategies. Methods: The study outcomes were synthesized using a narrative approach. Relevant records were gathered through Boolean searches in databases such as PubMed, Scopus, and Web of Science. A total of 21 articles related to multi-agent AI, healthcare, and ethical issues are included in this review. Results: Seven key ethical challenges were identified: (1) compound opacity, where interacting AI agents create layers of inscrutable decision-making; (2) error propagation and attribution difficulties, complicating accountability for clinical harm; (3) increased clinician dependence and automation bias, leading to potential deskilling and overreliance; (4) erosion of human oversight, as multi-agent AI systems operate beyond effective human control; (5) privacy and data security risks, stemming from complex data flows among agents; (6) threats to patient autonomy and informed consent, due to opaque or paternalistic AI recommendations; and (7) contextual blindness, reflecting a loss of individualized patient understanding in modular AI workflows. Furthermore, this review also summarized solutions proposed in the existing literature for these ethical issues. Conclusions: Multi-agent AI systems intensify existing ethical concerns in healthcare by distributing decision-making and blurring responsibility. To mitigate these issues, recent research advocates for the development of adaptive governance models, clear accountability frameworks, human–AI collaboration structures that preserve clinician authority, enhanced systems for explainability, and privacy-centered designs. In order to successfully incorporate agentic AI into healthcare, it is essential to maintain transparency, protect patient rights, and ensure that human-centered values continue to guide clinical decision-making in an era dominated by autonomous, interacting AI systems.

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