A foundational architecture for AI agents in healthcare.

Abstract

Medical AI agents represent a transformative paradigm in healthcare, distinguished from traditional AI by their autonomy, adaptability, and ability to manage complex tasks. This review introduces a conceptual framework for these agents built on four core components: planning, action, reflection, and memory. We examine the framework's application across key clinical domains, from enhancing diagnostic accuracy and personalizing treatment to guiding robotic surgery and enabling real-time patient monitoring. The review critically analyzes implementation challenges, including technical integration, clinician adoption, regulatory adaptation, and ethical considerations like data privacy and algorithmic bias. Future directions are explored, including the shift toward proactive, multi-agent collaborative systems and the visionary AI Agent Hospital concept. While these agents hold immense potential to revolutionize healthcare delivery by improving efficiency and patient outcomes, their successful and equitable integration hinges on navigating these profound technical, ethical, and regulatory hurdles.