ClinDEF: A Dynamic Evaluation Framework for Large Language Models in Clinical Reasoning

ClinDEF is a dynamic evaluation framework specifically designed to assess the performance of large language models (LLMs) in clinical reasoning tasks. By simulating real clinical scenarios, using multi-dimensional metrics, and adopting an interactive process, it addresses the problem that traditional benchmark tests overlook the complexity of clinical reasoning, aiming to comprehensively test models' medical reasoning capabilities.

As LLMs are increasingly applied in the medical field, accurately evaluating their reasoning capabilities in real clinical scenarios has become a key challenge. Traditional benchmark tests focus on medical knowledge Q&A, while clinical reasoning requires integrating multi-source information (medical history, symptoms, lab results, etc.) and involves complex cognitive processes such as hypothesis generation and evidence weighing—needs that traditional methods struggle to meet.

ClinDEF (Clinical Dynamic Evaluation Framework) is designed with the core concept of simulating reasoning processes in real clinical environments, adopting a dynamic evaluation paradigm. Unlike one-time Q&A, it tests models' clinical thinking chains through multi-round interactions and progressive information disclosure, which is closer to the real consultation process between doctors and patients.

ClinDEF evaluates models from four dimensions:

1. Information Integration Capability: Extract key information from multi-source data and establish connections;
2. Hypothesis Generation and Verification: Propose reasonable diagnostic hypotheses and verify/exclude them through subsequent information;
3. Differential Diagnosis Capability: Distinguish different diseases with similar clinical manifestations;
4. Integrity of Reasoning Chain: Demonstrate a clear reasoning path and explain the basis for decisions.

The dynamic nature of ClinDEF lies in its interactive process: initially, only limited information (chief complaint, basic medical history) is provided, and the model can actively ask for needed information to simulate the information collection process of real consultations. The advantages of this mechanism include: being close to real clinical scenarios, testing the model's information acquisition strategy, and evaluating performance stability under different information conditions. The evaluation process records the model's information requests, reasoning steps, and conclusions, forming a complete trajectory for scoring and analysis.

ClinDEF is of great significance to the medical AI field:

• Provides developers with a standardized evaluation tool to support the research and development and quality control of medical LLMs;
• Serves as a reference framework for the clinical access of AI systems in medical institutions to determine whether models have auxiliary decision-making capabilities;
• Used to continuously monitor performance changes of deployed systems and timely detect degradation or deviations;
• Provides an experimental platform for research to help understand the advantages and limitations of AI technology.

ClinDEF currently has limitations: it is mainly based on text cases and does not fully integrate multi-modal data such as medical images and laboratory values; the evaluation scenarios focus on diagnostic reasoning, with limited coverage of treatment decisions and prognosis assessment. Future directions include: expanding evaluation dimensions to more clinical tasks, introducing multi-modal data support, building large-scale evaluation datasets, and developing specialized assessment modules for specific medical specialties.