Enabling Responsible AI Agents in Healthcare: A Comprehensive Framework for Clinical Integration, Triage, and Personalized Service Delivery

Abstract

Objective: To present a framework for responsible implementation and evaluation of AI Agents in clinical service delivery, focusing on their potential to enhance healthcare efficiency, improve diagnostic accuracy, and personalize patient care.Materials and Methods: We outline a six-part framework for developing AI agents, including foundation model selection, adaptation for a healthcare domain, integration with third-party tools, hosting and infrastructure details, software stack design, data security and privacy considerations, and performance and evaluation.Results: We demonstrate our framework through an application on the example of a triage and scheduling AI agent developed for a hypothetical specialist medical clinic, illustrating key trade-offs and decisions throughout the system development and including illustrative code listings demonstrating how various system components come together in practice.Discussion: We highlight the transformative potential of AI agents in healthcare while addressing critical ethical considerations, including bias mitigation, transparency requirements, and patient privacy protection. Implementation challenges encompass technical barriers, organizational resistance, and regulatory compliance needs.Conclusion: The framework provides a comprehensive approach for healthcare institutions to implement AI agents effectively, demonstrating their potential to enhance clinical service delivery through improved efficiency, better decision support, and personalized patient care. We emphasize the need for continued research, collaborative data sharing, and supportive regulatory frameworks to advance AI integration in healthcare settings.

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