  Large Language Models (LLMs) have demonstrated remarkable proficiency in
human interactions, yet their application within the medical field remains
insufficiently explored. Previous works mainly focus on the performance of
medical knowledge with examinations, which is far from the realistic scenarios,
falling short in assessing the abilities of LLMs on clinical tasks. In the
quest to enhance the application of Large Language Models (LLMs) in healthcare,
this paper introduces the Automated Interactive Evaluation (AIE) framework and
the State-Aware Patient Simulator (SAPS), targeting the gap between traditional
LLM evaluations and the nuanced demands of clinical practice. Unlike prior
methods that rely on static medical knowledge assessments, AIE and SAPS provide
a dynamic, realistic platform for assessing LLMs through multi-turn
doctor-patient simulations. This approach offers a closer approximation to real
clinical scenarios and allows for a detailed analysis of LLM behaviors in
response to complex patient interactions. Our extensive experimental validation
demonstrates the effectiveness of the AIE framework, with outcomes that align
well with human evaluations, underscoring its potential to revolutionize
medical LLM testing for improved healthcare delivery.
