Beyond Accuracy: Assessing LLMS' Ability to Recognize Their Limits in Medical Decision-Making

Abstract

While Large Language Models (LLMs) demonstrate impressive medical capabilities through Retrieval-Augmented Generation (RAG) and domain optimization, a critical question remains: can LLMs autonomously recognize when to seek external help rather than provide independent medical recommendations? This metacognitive capability is essential for safe healthcare deployment. To address this gap, we introduce a novel evaluation framework assessing LLMs' autonomous help-seeking behavior through three workflows: Force-RAG (mandated external retrieval), No-RAG (internal knowledge only), and Auto-RAG (autonomous decision-making). Our comprehensive evaluation of 13 LLMs configurations across six clinical departments using 954 real-world cases reveals three key insights: (1) larger models don't necessarily exhibit superior help-seeking calibration; (2) reasoning strategies significantly impact metacognitive performance across medical domains; (3) proprietary models demonstrate superior autonomy in balancing self-reliance with appropriate help-seeking. These findings challenge conventional scaling assumptions and establish help-seeking behavior as fundamental to medical AI reliability.

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