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  1. Home
  3. Towards Autonomous Medical Artificial Intelligence Agents.
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Related Experiment Video

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Published on: December 23, 2025

Towards autonomous medical artificial intelligence agents.

Dyke Ferber1,2, Lars Hilgers2,3, Christiane Höper2

  • 1Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Heidelberg, Germany.

Nature
|June 17, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Medical Intelligence for Reasoning and Action (MIRA), an AI agent, demonstrated superior diagnostic accuracy and safer clinical decisions than physicians in EHR simulations. This AI shows potential for advanced clinical decision support integrated into healthcare workflows.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Electronic Health Records

Background:

  • Large language models (LLMs) show promise for clinical decision-making but are often limited to specific tasks.
  • Integrating LLMs into electronic health records (EHRs) requires governed data access and safety constraints for clinical workflows.
  • The performance of AI agents in managing complex patient cases within an EHR environment remains largely unproven.

Purpose of the Study:

  • To evaluate the capabilities of an autonomous AI agent, MIRA, operating within a sandboxed EHR environment.
  • To determine if MIRA can achieve physician-level performance in managing patient cases, including data retrieval, test ordering, diagnosis, and treatment planning.
  • To compare MIRA's performance against human physicians in terms of diagnostic accuracy and clinical decision-making.

Main Methods:

  • MIRA, an autonomous AI agent, was developed to operate within a simulated EHR environment.
  • The AI navigated a broad clinical action space, including obtaining patient histories, ordering and interpreting tests, generating differential diagnoses, and formulating treatment plans.
  • Simulations were conducted using real patient cases across various diagnoses, comparing MIRA's performance to that of physicians.

Main Results:

  • MIRA demonstrated superior diagnostic accuracy compared to physicians in simulated patient cases.
  • The AI made guideline-concordant, medication-safe, and appropriate admission decisions.
  • MIRA successfully translated clinical intent into structured, actionable EHR operations, outperforming previous LLM applications focused on isolated tasks.

Conclusions:

  • An EHR-integrated AI agent like MIRA can effectively manage patient cases and outperform physicians in specific metrics.
  • MIRA shows potential as a decision-support partner by enabling structured, actionable EHR operations.
  • Further prospective, real-world studies are necessary to confirm generalization, safety, and governance of such AI systems.