Comprehensive Medical Benchmark in Chinese (CMB): Evaluating Large Language Models for Chinese Medical Knowledge and Reasoning

Core Concepts

The authors propose a localized medical benchmark called CMB (Comprehensive Medical Benchmark in Chinese) to evaluate the performance of large language models (LLMs) in the Chinese medical domain, which includes both multiple-choice questions and complex clinical diagnostic cases. The benchmark aims to assess LLMs' medical knowledge and reasoning capabilities within the Chinese linguistic and cultural framework.

Abstract

The authors have developed a comprehensive medical benchmark called CMB to evaluate the performance of large language models (LLMs) in the Chinese medical domain. The benchmark consists of two parts:

CMB-Exam: This subset contains multiple-choice questions from various medical qualification exams, covering four clinical professions (physicians, nurses, medical technicians, and pharmacists) as well as undergraduate disciplines and graduate entrance exams. The questions span the entire professional journey, from basics to advanced levels.

CMB-Clin: This subset focuses on complex clinical diagnostic cases, requiring the models to synthesize knowledge and engage in reasoning to provide informed responses. The cases are derived from real-world medical records and curated by medical experts.

The authors have evaluated several prominent LLMs, including ChatGPT, GPT-4, dedicated Chinese LLMs, and LLMs specialized in the medical domain. The key findings include:

GPT-4 and recent open-sourced Chinese LLMs like Qwen-72B-Chat and Yi-34B-Chat have achieved an accuracy rate exceeding 60% on the CMB-Exam, surpassing the threshold required for obtaining a medical license.

Accuracy exhibits significant disparities across professional levels and knowledge areas, with traditional Chinese medicine and Western medicine showing notable differences.

The effectiveness of the Chain-of-Thought (CoT) and few-shot prompts varies among models with different accuracy levels, especially in knowledge-intensive tasks.

The results of automatic evaluation using GPT-4 highly agree with expert evaluation results on the CMB-Clin subset.

The authors hope that the CMB benchmark will provide valuable insights into the current state of LLMs in the Chinese medical domain and facilitate the widespread adoption and enhancement of medical LLMs within China.

Stats

The CMB dataset comprises 280,839 multiple-choice questions across 6 major categories and 28 subcategories.
The CMB-Clin subset includes 74 expert-curated medical record consultations, with 208 complex clinical diagnostic questions.

Quotes

"The establishment of a standardized medical benchmark becomes a fundamental cornerstone to measure progression."
"Merely translating English-based medical evaluation may result in contextual incongruities to a local region."
"The clinical diagnostic questions are based on real, intricate cases, with correct answers determined by a consensus of teaching experts."

Key Insights Distilled From

CMB

by Xidong Wang,... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2308.08833.pdf

Deeper Inquiries

How can the CMB benchmark be further expanded or refined to better capture the nuances of traditional Chinese medicine and its integration with Western medical practices?

To better capture the nuances of traditional Chinese medicine (TCM) and its integration with Western medical practices, the CMB benchmark can be expanded or refined in the following ways:

Incorporating TCM-specific Evaluation Criteria: Develop evaluation criteria that specifically assess the understanding and application of TCM principles, diagnostic methods, and treatment modalities. This can include questions related to TCM theory, herbal medicine, acupuncture, and other traditional practices.

Collaboration with TCM Experts: Collaborate with TCM experts to ensure the authenticity and accuracy of TCM-related questions and scenarios included in the benchmark. Their insights can help in designing relevant and challenging evaluation tasks.

Diversifying Case Studies: Include a wider range of case studies that reflect the integration of TCM and Western medicine in clinical practice. These cases can highlight the complementary nature of both systems and the holistic approach to patient care.

Language and Terminology: Ensure that the language used in the benchmark aligns with TCM terminology and concepts. This will help in accurately assessing a model's understanding of TCM-specific terms and principles.

Feedback Mechanisms: Implement feedback mechanisms where TCM practitioners can provide input on the accuracy and relevance of TCM-related questions. This continuous feedback loop can help in refining the benchmark over time.

What are the potential ethical and regulatory considerations in deploying large language models in the Chinese medical domain, and how can these challenges be addressed?

In deploying large language models (LLMs) in the Chinese medical domain, several ethical and regulatory considerations need to be addressed:

Patient Privacy and Data Security: Ensuring the protection of patient data and privacy is paramount. LLMs must comply with data protection regulations and implement robust security measures to safeguard sensitive medical information.

Bias and Fairness: Addressing bias in LLMs to ensure fair and equitable outcomes for all patients. Regular audits and bias assessments can help identify and mitigate any biases present in the models.

Transparency and Accountability: Providing transparency in how LLMs make decisions and ensuring accountability for their recommendations. Clear documentation of model training data, algorithms, and decision-making processes is essential.

Regulatory Compliance: Adhering to regulatory frameworks governing the use of AI in healthcare, such as obtaining necessary approvals from regulatory bodies and ensuring compliance with medical standards and guidelines.

Informed Consent: Obtaining informed consent from patients before using LLMs in their care. Patients should be informed about the use of AI technologies and their implications on diagnosis and treatment.

To address these challenges, stakeholders in the Chinese medical domain can:

Establish clear guidelines and protocols for the ethical use of LLMs in healthcare.
Provide ongoing training and education on AI ethics and regulations for healthcare professionals.
Foster collaboration between AI developers, healthcare providers, and regulatory authorities to ensure ethical AI deployment.
Implement robust governance structures to oversee the use of LLMs and address ethical concerns proactively.

How can the insights gained from the CMB benchmark be leveraged to develop more effective and culturally-relevant medical AI systems for other regions or ethnic groups with distinct medical traditions?

The insights gained from the CMB benchmark can be leveraged to develop more effective and culturally-relevant medical AI systems for other regions or ethnic groups with distinct medical traditions in the following ways:

Customization and Localization: Tailor AI models to incorporate specific medical practices, terminology, and cultural nuances of the target region or ethnic group. This customization can enhance the relevance and accuracy of the AI system.

Collaboration with Local Experts: Engage with local healthcare professionals, researchers, and traditional medicine practitioners to gain insights into the unique healthcare practices and beliefs of the community. Their expertise can guide the development of culturally-sensitive AI systems.

Data Collection and Validation: Collect diverse and representative data sets that reflect the healthcare challenges and priorities of the target population. Validate the AI system using local data to ensure its effectiveness in real-world scenarios.

Interdisciplinary Approach: Foster collaboration between AI experts, healthcare professionals, anthropologists, and sociologists to understand the social and cultural factors that influence healthcare decisions. This holistic approach can lead to the development of more contextually relevant AI systems.

Continuous Learning and Improvement: Implement feedback mechanisms and continuous learning loops to adapt the AI system based on user feedback and evolving healthcare practices. This iterative process can enhance the system's performance and cultural relevance over time.