Autonomous AI Agents in Healthcare: From Diagnosis to Clinical Workflow Automation

Dear Colleagues,

Recent progress in large language models (LLMs) and agentic artificial intelligence is reshaping how healthcare systems support clinical reasoning, communication, and operational workflows. Unlike traditional predictive models limited to a single task, autonomous AI agents can plan multi-step actions, retrieve and synthesize patient data, interact with electronic health records and clinical tools, and coordinate across diagnostic, documentation, and care-delivery pipelines with limited human oversight. These systems hold promise for accelerating diagnosis, reducing clinician documentation burden, improving triage accuracy, and streamlining care coordination across multidisciplinary teams, while also raising new engineering questions about safety, reliability, interpretability, scalability, and regulatory oversight in real clinical environments.

This Special Issue invites original research, reviews, and case studies on the design, validation, and deployment of autonomous AI agents and LLM-powered pipelines in healthcare. Topics of interest include, but are not limited to, agentic architectures for clinical decision support, LLM-based diagnostic and triage assistants, automated clinical documentation and summarization, multi-agent systems for care coordination and workflow automation, retrieval-augmented and tool-using clinical agents, safety, hallucination mitigation, human-in-the-loop oversight, and real-world clinical validation and deployment studies. Contributions spanning diagnostics, nursing, surgery, and hospital operations are welcome.

Dr. Amin Ramezani
Guest Editor

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