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September 10, 2025Open Access

Agentic memory-augmented retrieval and evidence grounding for medical question-answering tasks

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The agent-based system outperforms standalone LLMs in medical QA tasks, suggesting improved accuracy with evidence grounding.

The system achieved 82.98% and 86.24% accuracy on USMLE Step 1 and Step 2, respectively.
Comparative evaluations showed the agentic system surpassing or closely matching state-of-the-art medical LLMs.
It employs a memory bank for efficient long-context inference beyond standard LLM capabilities.
The results indicate a promising approach for reliable medical AI systems through dynamic reasoning.

Jia et al. (2025) studied this question.

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September 10, 2025Open Access

View Full Paper

Ask AI

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Authors

The agent-based system outperforms standalone LLMs in medical QA tasks, suggesting improved accuracy with evidence grounding.

The system achieved 82.98% and 86.24% accuracy on USMLE Step 1 and Step 2, respectively.
Comparative evaluations showed the agentic system surpassing or closely matching state-of-the-art medical LLMs.
It employs a memory bank for efficient long-context inference beyond standard LLM capabilities.
The results indicate a promising approach for reliable medical AI systems through dynamic reasoning.

Jia et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark