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September 16, 2025Open Access

Large Language Model Agents for Biomedicine: A Comprehensive Review of Methods, Evaluations, Challenges, and Future Directions

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Authors

XXXiaoran XuUniversity of South FloridaRSRavi SankarTexas Tech University

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Implication

This review examines large language model agents in clinical applications, highlighting challenges and evaluation methods.

Key Points

  • Large language model agents enhance decision-making in biomedical research, offering new capabilities for clinical settings.
  • The review identifies key challenges such as interpretability and data bias, impacting the use of LLM agents in healthcare.
  • Assessment of agent performance is evaluated under dynamic, interactive conditions, indicating the need for effective benchmarks.
  • Future directions focus on improving human–AI collaboration and addressing issues like hallucinations and regulatory gaps.

Cite This Study

Xu et al. (2025) studied this question.

synapsesocial.com/papers/68d42336713b0b5dfea6b92bhttps://doi.org/10.22541/au.175795684.47167615/v1
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