Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm

TL;DR

An automatic evaluation paradigm tailored to assess the large language models' capabilities in delivering clinical services, e.g., disease diagnosis and treatment, is proposed and can be extended to any similar clinical scenarios to automatically evaluate the LLMs' medical capabilities.

Abstract

Large language models (LLMs) are gaining increasing interests to improve clinical efficiency for medical diagnosis, owing to their unprecedented performance in modelling natural language. Ensuring the safe and reliable clinical applications, the evaluation of LLMs indeed becomes critical for better mitigating the potential risks, e.g., hallucinations. However, current evaluation methods heavily rely on labor-intensive human participation to achieve human-preferred judgements. To overcome this challenge, we propose an automatic evaluation paradigm tailored to assess the LLMs' capabilities in delivering clinical services, e.g., disease diagnosis and treatment. The evaluation paradigm contains three basic elements: metric, data, and algorithm. Specifically, inspired by professional clinical practice pathways, we formulate a LLM-specific clinical pathway (LCP) to define the clinical capabilities that a doctor agent should possess. Then, Standardized Patients (SPs) from the medical education are introduced as the guideline for collecting medical data for evaluation, which can well ensure the completeness of the evaluation procedure. Leveraging these steps, we develop a multi-agent framework to simulate the interactive environment between SPs and a doctor agent, which is equipped with a Retrieval-Augmented Evaluation (RAE) to determine whether the behaviors of a doctor agent are in accordance with LCP. The above paradigm can be extended to any similar clinical scenarios to automatically evaluate the LLMs' medical capabilities. Applying such paradigm, we construct an evaluation benchmark in the field of urology, including a LCP, a SPs dataset, and an automated RAE. Extensive experiments are conducted to demonstrate the effectiveness of the proposed approach, providing more insights for LLMs' safe and reliable deployments in clinical practice.

Figures (9)

• Figure 1: The proposed evaluation paradigm. Metric: Clinical practice pathways are introduced from the medical education as the evidence of clinical capabilities. Data: Standardized patients are taken as the template for data collection. Algorithm: Retrieval-Augmented Evaluator can support a comprehensive and automated evaluation.
• Figure 2: A simplified example of structural SPs' medical records. Some details are omitted due to the limited space, such as the report results (denoted by XXX). Category and item are for bi-level retrieval, respectively. The full-version example can be seen in Table \ref{['tab:sps']} of the Appendix.
• Figure 3: Example of different medical tasks. The retrieval task (green font) is used to construct data format, which can be further exploited for automatic evaluations (red font). The data source for the retrieval task is SPs data, as shown in Figure \ref{['img:sps']}.
• Figure 4: Overview of the multi-agent framework. Intent recognition aims to understand the doctor agent's query for terminating conversation. Query parser can map the doctor agent's query to bi-level structure. The multi-agent framework can achieve context generation for the clinical QA and reasoning tasks, as well as environment simulation for the diagnostic dialogue tasks. Besides, RAE can automatically evaluate the doctor agent's clinical capabilities.
• Figure 5: Comparisons of Medical Test Guidance.

Theorems & Definitions (1)

• definition 1