Medical AI agents in health care: architecture, clinical translation, and trustworthy implementation

Clinical care requires iterative evidence acquisition, revision of working hypotheses, and action under incomplete information, changing patient states, and finite resources. Yet medical artificial intelligence (AI) is still evaluated largely through recognition, prediction, content generation, or static question answering. The central scientific question is whether model capability can be translated into safe, effective, and accountable longitudinal clinical action. A medical agent is not merely a large language model (LLM), but a sociotechnical system integrating models, knowledge, data, memory, tools, workflows, human oversight, and governance. Drawing on representative studies, authoritative reviews, reporting guidance, and regulatory documents available through July 2026, this Review proposes a six-module closed-loop architecture—perception, knowledge, reasoning, planning, action, and feedback—clarifies boundaries with related AI systems, and links clinical use cases to evidence and governance requirements. Retrieval-augmented generation, tool use, reflection, and multi-agent collaboration have improved performance in selected controlled tasks. However, static medical question-answering scores are weak proxies for interactive clinical performance, and recent benchmarks show only modest accuracy gains despite substantial increases in token use and latency. A small number of randomized trials and real-world workflow studies suggest potential benefits for documentation, trial prescreening, and bounded decision processes, but do not support unsupervised autonomous diagnosis or treatment. We therefore propose four risk-calibrated autonomy levels, a six-stage evidence pathway, and lifecycle governance. Clinical value should be judged by the net benefit of the clinician–agent–workflow system. The near-term goal is not clinician-independent autonomy, but clinician-led, patient-engaged, evidence-constrained, and accountable human–agent care.

Key Words: medical AI agent; large language model; multi-agent system; clinical translation; human–AI collaboration; trustworthy artificial intelligence; lifecycle governance