ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World

## Abstract

Abstract Large language models (LLMs) have achieved significant performance progress in various natural language processing applications. However, LLMs still struggle to meet the strict requirements for accuracy and reliability in the medical field and face many challenges in clinical applications. Existing clinical diagnostic evaluation benchmarks for evaluating medical agents powered by LLMs have severe limitations.
Firstly, most existing medical evaluation benchmarks face the risk of data leakage or contamination. Secondly, existing benchmarks often neglect the characteristics of multiple departments and specializations in modern medical practice. Thirdly, existing evaluation methods are limited to multiple-choice questions, which do not align with the real-world diagnostic scenarios and are not robust. Lastly, existing evaluation methods lack comprehensive evaluations of end-to-end real clinical scenarios. These limitations in benchmarks in turn obstruct advancements of LLMs and agents for medicine.
To address these limitations, we introduce ClinicalLab , a comprehensive clinical diagnosis agent alignment suite. ClinicalLab includes ClinicalBench , an end-to-end multi-departmental clinical diagnostic evaluation benchmark for evaluating medical agents and LLMs. ClinicalBench is based on real cases that cover 24 departments and 150 diseases. We ensure that ClinicalBench does not have data leakage. ClinicalLab also includes four novel metrics ( ClinicalMetrics ) for evaluating the effectiveness of LLMs in clinical diagnostic tasks. We evaluate 17 general and medical-domain LLMs and find that their performance varies significantly across different departments. Based on these findings, in ClinicalLab, we propose ClinicalAgent , an end-to-end clinical agent that aligns with real-world clinical diagnostic practices. We systematically investigate the performance and applicable scenarios of variants of ClinicalAgent on ClinicalBench. Our findings demonstrate the importance of aligning with modern medical practices in designing medical agents.
The code and dataset are publicly available at https://github.com/WeixiangYAN/ClinicalLab .

## 1 Introduction

Large Language Models (LLMs) have demonstrated exceptional capabilities across a wide variety of natural language processing tasks [, , ]. Recent studies attempt to apply LLMs to the medical field [], aiming to improve the efficiency of healthcare systems through early disease diagnosis and timely health management, thereby reducing the workload of medical professionals. However, the high sensitivity of the medical field and the strict requirements for accuracy and reliability in clinical decision-making pose great challenges to LLMs. Recent studies find that LLMs are prone to producing hallucinations in medical diagnoses [, ], and such erroneous diagnoses could harm the physical and psychological health of patients and also potentially lead to serious medical negligence. How to effectively, reliably, and comprehensively evaluate the true capabilities of LLMs and the accuracy of their diagnostic results, as well as reveal their limitations to avoid potential misdiagnosis, remains an unresolved problem.

Recent studies find that existing benchmarks cannot effectively evaluate the medical capabilities of LLMs [, ].
(1) Firstly, existing benchmarks are often based on data collected from online consultation platforms or medical textbooks [], which could easily be included in the training data of LLMs, that is, leading to data leakage or contamination and thus biasing the performance evaluation of LLMs. (2) Secondly, the departmental setup in modern medicine is designed to address the complex medical needs of different structures and functions of human organs. The specific skills and treatment methods vary significantly across different departments. However, existing evaluation benchmarks overlook the characteristics of multi-departmental and highly specialized nature of modern medicine, hence they are insufficient in capturing performance differences across departments. (3) Thirdly, existing evaluation methods typically confine themselves to multiple-choice questions [, , ], which does not align with real-world clinical diagnostic scenarios. In actual medical environments, patients seek medical services because they are uncertain about their health conditions, rather than knowing the possible disease options and then seeking a doctor’s judgment. Besides, recent studies [] have demonstrated that using multiple-choice questions to evaluate LLMs is not robust and introduces significant biases. (4) Last but not least, there is currently no evaluation method that can comprehensively and reliably evaluate the end-to-end practicality of LLMs in the entire clinical diagnosis process, which starts from the moment a patient enters the clinic and ends when the patient is discharged. This issue will, in turn, limit the design and evaluation of practical medical agents powered by LLMs and harm the exploitation of the full potential of LLMs.

To address these limitations, we introduce ClinicalBench, an end-to-end multi-departmental clinical diagnostic evaluation benchmark for effectively and comprehensively evaluating the clinical diagnostic capabilities of LLMs. ClinicalBench is based on real cases that cover 24 departments and 150 diseases. ClinicalBench consists of 8 clinical diagnostic tasks. We ensure that ClinicalBench does not have data leakage. We evaluate the clinical diagnostic capabilities of LLMs in two dimensions. The task dimension measures the performance of each model in different tasks, while the department dimension evaluates the performance difference of each model across various medical specialties. Additionally, we propose four novel metrics (ClinicalMetrics) to precisely measure the effectiveness of LLMs in department guide and their clinical diagnostic capabilities. We evaluate 17 mainstream LLMs on ClinicalBench. The evaluation results reveal their performance in different departmental scenarios, reflecting their strengths and weaknesses in simulating human medical practice. We find that different LLMs typically excel in different departmental areas, and no single LLM can perform excellently well in all departmental domains. This behavior conforms to the needs of modern medical specialization.

Figure: Figure 1: The workflow diagram of ClinicalAgent. ClinicalAgent covers the entire process starting from the moment a patient enters the clinic and ending when the patient is discharged, which includes six key steps: 1) department guide; 2) preliminary consultation; 3) laboratory examination; 4) imageological examination; 5) final consultation; 6) medical treatment.
Refer to caption: x1.png

Inspired by the findings, we propose ClinicalAgent, a novel clinical diagnostic agent that dynamically allocates the $\mathcal{K}$ most relevant departments and assigns $\mathcal{N}$ clinicians from each department for a collaborative consultation based on the patient’s chief complaint. Leveraging flexible department scheduling and clinician allocation strategies, ClinicalAgent delivers a unified diagnostic result. Figure 1 depicts the workflow of ClinicalAgent. Experiments show that ClinicalAgent outperforms top-performing LLMs in clinical diagnostic performance, demonstrating the importance of aligning with modern medical practice for advancing agentic medical systems. We release ClinicalLab, a comprehensive clinical diagnosis agent alignment suite, including ClinicalBench, ClinicalMetrics, and ClinicalAgent, to promote development of clinical diagnostic agents.
Our contributions can be summarized as follows:

- •
ClinicalBench: We establish the first real-case-based, data-leakage-free, end-to-end multi-departmental benchmark for evaluating the clinical diagnostic capabilities of LLMs. This benchmark covers most departments (totalling 24) and most
varieties of diseases (totalling 150).
- •
ClinicalMetrics: We introduce four novel metrics to evaluate the practicality and effectiveness of the results generated by LLMs in real clinical scenarios.
- •
ClinicalAgent: We propose a clinical diagnostic agent tailored for modern medical settings. It surpasses competitive LLMs in the ClinicalBench evaluation and provides comprehensive validation and analysis of its practicality.

## 2 Related Work

### 2.1 Existing Medical Benchmarks

The Chinese Medical Benchmark [] consists of CMB-Exam and CMB-Clin, with data collected from various public examination databases and multiple-choice questions from textbooks. MedBench [] also includes multiple-choice questions from the Chinese mainland medical licensing examination and 1,025 QA pairs based on electronic medical records. MMedBench [] is a multilingual medical evaluation benchmark covering six languages,
with data sourced from medical textbooks and open-source medical websites in different languages.
Different from our method of collecting real cases from different departments, MMedBench and CMExam [] utilize GPT-4 to categorize each question according to a pre-defined list of departments. However, according to the experimental results shown in Table 4, GPT-4 exhibits significant errors in departmental classification.
MedQA [] consists of multiple-choice questions collected from professional medical board examinations. Its USMLE-style English subset offers four possible answer options for each question and is widely used to evaluate the performance of LLMs in the medical domain. PubMedQA [] is a biomedical QA dataset collected from the PubMed website, where questions need to be answered with Yes/No/Maybe.
MMLU [] is a
benchmark covering 57 tasks across multiple domains, recent work [] extracts 6 medicine-related tasks from MMLU to evaluate the medical capabilities of LLMs. Furthermore, HealthSearchQA [] is a dataset based on medical conditions and related symptoms frequently searched by users on search engines, aiming to evaluate a model’s ability to handle real-world user’s concerns about their health.

Table 1 compares statistics of existing benchmarks and datasets for evaluating LLMs in the medical field and our ClinicalBench, including the number of samples, task types, language coverage, and data sources. In summary, the main shortcomings of existing evaluation benchmarks include: (1) lack of end-to-end and evenly distributed departmental coverage to prevent evaluation bias; (2) data sources often come from easily accessible online consultation platforms, medical textbooks, and professional examinations, which poses high risks of data leakage; (3) existing benchmarks primarily evaluate medical knowledge through multiple-choice questions, which not only differ significantly from real-world diagnostic scenarios but also lack robustness.

Table: Table 1: Comparison between existing benchmarks and datasets for evaluating LLMs in the medical field.

### 2.2 Existing Agents for Medical Applications

Recent works attempt to solve medical and healthcare issues through paradigms of dividing tasks and collaboration among multiple agents. MedAgent [] is the first multi-agent framework in the medical field that improves the accuracy of solving medical multiple-choice questions by allowing the same LLM to play different roles in multi-round collaborative dialogues. However, the design of MedAgent still relies on the multiple-choice question format, which differs significantly from the diagnostic process in the real world.
On the other hand, williams2023epidemic [] effectively simulate real-world human behavior patterns through agents played by ChatGPT to address the challenge of incorporating human behavioral factors into epidemic models.
MDAgents [] is a multi-agent framework that employs an adaptive decision-making mechanism, addressing medical multiple-choice questions through multiple stages of checking problem complexity, dynamically recruiting experts, reasoning, and decision-making. Meanwhile, Agent Hospital [] improves diagnostic accuracy by simulating nearly all medical processes within a hospital and trains doctor agents through doctor-patient interaction simulations. However, due to the lack of a comprehensive dataset covering the entire medical process, currently its effectiveness is only validated on the MedQA multiple-choice dataset. In summary, existing medical agents suffer from severe limitations and constraints in designs and evaluations due to the lack of benchmarks and datasets based on real diagnostic processes. Therefore, developing an evaluation benchmark based on real medical diagnostic processes is critical. Furthermore, most previously proposed medical agents perform clinical diagnosis by using prompts to make the same model play different roles. In contrast, our ClinicalAgent is driven by the evaluation results and analysis of prior models on ClinicalBench. ClinicalAgent uses different models to play different roles and implements dynamic department scheduling and doctor allocation strategies for clinical diagnosis, ensuring a strong alignment with real hospital settings.

## 3 ClinicalBench: An End-to-End, Real-Case-based, Data-Leakage-Free Benchmark for Multi-Department Clinical Diagnostic Evaluation

In this section, we provide a detailed description and analysis of the ClinicalBench benchmark, including data sources and licensing information (Section 3.1), the data collection and quality control processes (Section 3.2), statistical overviews of the relevant datasets (Section 3.3), and descriptions of the 8 medical tasks (Section 3.4).

### 3.1 Data Sources & Licenses

The data samples used in the ClinicalBench benchmark are sourced from real clinical medical records of officially certified Grade 3A hospitals in China(^1^11Grade 3A hospitals are the highest level hospitals in China’s “three-grade, six-class” classification system.). The collection of this data strictly adheres to the principles of patient privacy protection. No information related to the hospitals is disclosed. As detailed in Data Processing & Quality, to protect patient privacy, any personally identifiable information (PII) of patients, treatment regions, or other sensitive information has been manually identified and removed by the team of doctors. All data is obtained legally and ethically, and has been reviewed and approved by the Ethics Committees of the relevant hospitals, ensuring that research activities on these data comply with ethical and legal obligations. In Appendix 8, we discuss the certification documents issued by medical institutions and notary offices.

We are committed to responsible data management and strictly follow relevant laws and regulations involving the collection, use, and distribution of protected health information.
To ensure the legal and regulated use of the dataset, we have formulated the ClinicalBench Usage and Data Distribution License Agreement, which can be found in the supplementary materials. This agreement strictly requires all users to use the data solely for research purposes and to adhere to strict regulations protecting patient privacy, prohibiting any form of personal information tracking or identification. Through these measures, we ensure the legality and ethics of data acquisition and use while supporting research that may promote the development of LLMs in clinical diagnostics. Appendix A.1 provides a detailed explanation of how we securely distribute the dataset.

Figure: Figure 2: The data management pipeline for ClinicalBench.
Refer to caption: x2.png

### 3.2 Data Processing & Quality

The ClinicalBench benchmark is manually created by three senior clinicians and two AI researchers. As shown in Figure 2, the creation process covers 4 key steps, as follows.
(1) The Data collection step focuses on authenticity, diversity, privacy. Based on department divisions and common disease types in each department, the medical team selects representative real cases for each disease from the hospital case database with permission for research.
Given that these clinical case data is the private information of hospitals, the risk of data leakage to any LLMs is completely eliminated.
(2) The Professional Knowledge Review step ensures the accuracy of the data. The team of doctors conducts a detailed professional review of the diagnostic information, treatment process, and results of each case to ensure the medical accuracy and proficiency of the data.
(3) The Privacy Protection and De-identification step ensures privacy protection. To protect patient privacy, the team of doctors conducts two rounds of independent reviews to identify and remove any content that could reveal patient identities, treatment regions, or other sensitive information.
(4) The Data Integrity and Compliance Check step aims for completeness and ethical compliance. Two AI researchers are responsible for reviewing the data to ensure that each record is complete, and meets the medical task requirements set in Section 3.4. Additionally, they reconfirm that the dataset does not contain any sensitive information and strictly complies with the ethical guidelines.

### 3.3 Data Statistics

ClinicalBench is a fine-grained evaluation benchmark based on chain-of-thought reasoning, specifically designed for multi-departmental clinical diagnosis, covering 24 departments such as pediatrics, orthopedics, and neurosurgery. Figure 4 presents detailed information about the various departments covered by ClinicalBench. It involves 150 different diseases, each comprising 10 specific cases, totaling 1500 samples, with an average of about 1000 tokens per case(^2^22All samples are originally in Chinese and translated into English using GPT-4 for reading and usage.). Table 1 provides relevant information about ClinicalBench. To the best of our knowledge, ClinicalBench is the most comprehensive clinical diagnostics evaluation benchmark to date, covering the widest range of departments and diseases.

Figure: Figure 3: Departments and distribution of case samples in ClinicalBench.
Refer to caption: x3.png

Each case $\mathcal{E}$ in ClinicalBench contains detailed clinical data, such as the patient’s gender, age(^3^33Gender and age are crucial factors in clinical diagnosis, and with PII and other sensitive information removed, they alone are typically not considered PII. Therefore, we decide to retain these two information.), chief complaint $\mathcal{CC}$, medical history $\mathcal{MH}$, physical examination $\mathcal{PE}$.
Additionally, the cases include various medical imaging reports $\mathcal{IR}$, such as X-rays, computed tomography (CT) scans, magnetic resonance imaging (MRI), and ultrasound examinations, as well as biochemical, immunological, microbiological, and pathological laboratory examination reports $\mathcal{LR}$ from biological samples such as blood, urine, and cerebrospinal fluid. The preliminary diagnosis, diagnostic basis, differential diagnosis, and treatment plans provided by doctors from each department collectively support a range of end-to-end clinical decision-making processes. A complete data example is provided in Table 5-9.

### 3.4 Task Overview

ClinicalBench systematically evaluates the end-to-end practicality of LLMs in clinical diagnosis by simulating the complete patient visit process, from the patient’s entry into the hospital to their discharge. We divide the entire process into 8 specific tasks, covering various stages from preliminary reception to final diagnosis and treatment plan formulation. These tasks are categorized into three main functional groups: department guide, clinical diagnosis, and imaging diagnosis.

#### 3.4.1 Department Guide (Multi-Choice QA with 24 options)

When patients first enter the hospital, guiding them to the correct department for further consultation, examination and treatment is crucial for providing targeted medical services. The Department Guide (DG) task requires the model to choose the most reasonable department from a given set of 24 departments based on a $\mathcal{CC}$, i.e., $\mathcal{DP}=\text{DG}(\mathcal{CC})$, where $\mathcal{DP}$ denotes the selected department. This task evaluates the decision-making accuracy, and instruction-following capability of LLMs in real-world medical scenarios.

#### 3.4.2 Clinical Diagnosis (Generative QA)

The Clinical Diagnosis task evaluates the LLM’s clinical diagnostic capabilities in real cases and its analysis and interpretation abilities. Clinical Diagnosis includes 6 sub-tasks, each designed according to the standard diagnostic process recommended by the Chinese official record [] and using the chain-of-thought pattern. The 6 sub-tasks are defined as follows.

Preliminary Diagnosis (PD): The model generates a list of possible diseases $\mathcal{P}$ based on $\mathcal{CC}$, $\mathcal{MH}$, and $\mathcal{PE}$, i.e., $\mathcal{P}=\text{PD}(\mathcal{CC,MH,PE})$. This task primarily evaluates the model’s information synthesis and reasoning abilities.

Diagnostic Basis (DB): The model needs to provide supportive medical evidence $\mathcal{B}$ for each possible disease in $\mathcal{P}$, i.e., $\mathcal{B}=\text{DB}(\mathcal{E,P})$. This task mainly evaluates the model’s evidence extraction ability and helps mitigate hallucinations.

Differential Diagnosis (DD): After considering $\mathcal{B}$, the model should perform a differential analysis $\mathcal{D}$ to exclude other diseases that have different causes but similar manifestations, i.e., $\mathcal{D}=\text{DD}(\mathcal{E,P,B})$. This task primarily evaluates the model’s abilities in analytical comparison and decision-making.

Final Diagnosis (FD): Integrating all information, the model needs to determine the final diagnosis $\mathcal{F}$, i.e., $\mathcal{F}=\text{FD}(\mathcal{E,P,B,D})$. This task mainly evaluates the model’s comprehensive judgment ability and the accuracy of clinical diagnosis.

Principle of Treatment (PT): The model determines the treatment principles and guidelines $\mathcal{G}$ for $\mathcal{F}$, i.e., $\mathcal{G}=\text{PT}(\mathcal{F})$. This task primarily evaluates the model’s memorization of medical standard operating procedures and knowledge points.

Treatment Plan (TP): The model formulates specific treatment steps and plans $\mathcal{T}$ for the given case $\mathcal{E}$, including medication, surgical intervention, and physical therapy, i.e., $\mathcal{T}=\text{TP}(\mathcal{E,P,B,D,F,G})$. This task mainly evaluates the model’s abilities in knowledge application and strategic planning.

#### 3.4.3 Imaging Diagnosis (Generative QA)

The Imaging Diagnosis (ID) task requires LLMs to understand detailed textual reports of medical images, identify key features of lesions, such as tumors, inflammation, or other pathological changes, and provide imaging diagnosis results $\mathcal{IDR}$, i.e., $\mathcal{IDR}=\text{ID}(\mathcal{IR})$. This task evaluates the LLM’s ability to analyze and interpret medical imaging reports for diagnostic support.

## 4 Experiments of LLMs on ClinicalBench

### 4.1 Models

To comprehensively analyze strengths and weaknesses of a broad selection of competitive LLMs on different tasks and departments in ClinicalBench, we evaluate 11 general LLMs, including GPT-4 [], GPT-3.5 [], Gemini-Pro-1.0 [], InternLM2-20B-Chat [], Yi-34B-Chat [], Qwen-72B-Chat [], Baichuan2-13B-Chat [], Claude3 [], ChatGLM3-6B [], BlueLM-7B-Chat [], and Spark-3 []. We also evaluate 6 Chinese medical LLMs, including HuatuoGPT2-34B [], WiNGPT2-14B-Chat [], PULSE-20B [], Taiyi-LLM [], DISC-MedLLM[], and BianQue-2 []. To ensure the reproducibility of the experiments, we use greedy decoding for each LLM (set the temperature parameter to 0 0 or set the do_sample parameter to *False*).
The experimental evaluation is completed through API calls and 8 NVIDIA A6000 GPUs.

### 4.2 Evaluation Metrics (ClinicalMetrics)

We propose four novel metrics to precisely measure the effectiveness of LLMs in department guide and their clinical diagnostic capabilities, as follows.
Additionally, we use metrics such as accuracy, BLEU [], ROUGE [], and BertScore [] to evaluate the experimental results. For more details and the handling of medical synonyms, please refer to Appendix A.2.

Department Win Rate ($\mathsf{DWR}$) measures the relative performance of LLMs in clinical diagnosis across different medical departments, which is calculated as Eq. 1,

$$ $\mathsf{DWR}=\frac{1}{m}\sum_{j=1}^{m}\left(\frac{1}{n}\sum_{i=1}^{n}\left(n-( \mathsf{r}_{i}^{j}-1)\right)\right)$ (1) $$

where $m$ and $n$ denote the total number of departments and models, respectively; $r_{i}^{j}$ is the ranking of model $i$ in department $j$ (based on the Avg. score).

Department Instruction Following Rate ($\mathsf{DIFR}$) measures the degree to which LLMs follow task instructions when generating the quantity and names of ranked departments. It is the average of two sub-metrics: $\mathsf{DIFR\text{-}Q}$ and $\mathsf{DIFR\text{-}N}$.

Department Quantity Following Rate ($\mathsf{DIFR\text{-}Q}$) measures the consistency between the quantity of top $K^{\prime}$ departments generated by the model and the quantity of $K$ departments specified in the task instructions, which is calculated as Eq. 2,

$$ $\mathsf{DIFR\text{-}Q}=\frac{\sum_{i=1}^{N}\mathbf{S}_{\{K^{\prime}_{i}=K\}}}{N}$ (2) $$

where $\mathbf{S}_{\{K^{\prime}_{i}=K\}}$ is an indicator function that equals 1 if the quantity of $K^{\prime}_{i}$ departments generated for the $i$-th sample, matches the specified quantity $K$ in the task instructions, and 0 otherwise. $N$ is the total quantity of test samples.

Department Name Following Rate ($\mathsf{DIFR\text{-}N}$) measures the consistency of the department names generated by the model with the names in a predefined list of 24 departments, which is calculated as Eq. 3,

$$ $\mathsf{DIFR\text{-}N}=\frac{\sum_{i=1}^{N}\sum_{j=1}^{K^{\prime}_{i}}\text{ match}(i,j)}{\sum_{i=1}^{N}K^{\prime}_{i}}$ (3) $$

where $K^{\prime}_{i}$ represents the number of departments generated for the $i$-th sample, and the match function $\text{match}(i,j)$ checks whether the department name generated for the $j$-th position in the $i$-th sample is in the predefined list of departments. If so, $\text{match}(i,j)=1$; otherwise, it is 0. $N$ is the total number of test samples.

Comprehensive Diagnostic Rate ($\mathsf{CDR}$) measures the accuracy of LLMs in simultaneously predicting correct department guide and disease diagnosis, which is calculated as Eq. 4,

$$ $\mathsf{CDR}=\frac{\sum_{i=1}^{N}\text{S}_{\text{guide}}(i)\times\text{S}_{ \text{diagnosis}}(i)}{N}$ (4) $$

where $\text{S}_{\text{guide}}(i)$ and $\text{S}_{\text{diagnosis}}(i)$ are indicator functions of correctness for the $i$-th sample in the guide and diagnosis tasks, respectively. If the prediction is correct, the function value is 1, and 0 otherwise. $N$ is the total number of test samples.

Acceptability measures the comprehensive performance of LLMs in terms of prediction accuracy in department guide and disease diagnosis tasks, as well as the language quality of other generated diagnostic content, reflecting the overall effectiveness of the model in handling complex clinical situations. It is calculated as
Eq. 5,

$$ $\mathsf{Acceptability}=\left(\frac{1}{N}\sum_{i=1}^{N}\mathsf{CDR}(i)\right) \times\left(\frac{1}{NKM}\sum_{i=1}^{N}\sum_{k=1}^{K}\sum_{m=1}^{M}\text{ Metric}_{m}^{k}(i)\right)$ (5) $$

where $\text{CDR}(i)$ is the indicator function of the $i$-th sample correctly predicting both guide and diagnosis tasks. $\text{Metric}_{m}^{k}(i)$ represents the score of the $m$-th evaluation metric for the $i$-th sample in the $k$-th diagnostic task. $N$ is the total number of test samples. $M$ is the number of evaluation metrics, which include BLEU, ROUGE, and BertScore.
$K$ is the total number of diagnostic tasks, including diagnostic basis, differential diagnosis, principles of treatment, treatment plans, and imaging diagnosis.

### 4.3 Results & Analysis

Table: Table 2: Performance of LLMs across 8 medical tasks in ClinicalBench. The PD and FD tasks are evaluated based on Accuracy, while other Clinical Diagnosis subtasks are evaluated using the average of of BLEU, ROUGE, and BertScore scores. For an LLM, we compute the average (Avg.) score over all the 9 scores under the 8 tasks as its overall performance on ClinicalBench.

Table 2 shows the performance of 17 LLMs on ClinicalBench.
Note that all experiments in this work are conducted on the original Chinese samples of ClinicalBench(^4^44Due to high costs, we are unable to conduct these repetitive experiments, but we provide code to support the evaluation using the English version of ClinicalBench.).

Performance on each task. On the department guide task, InternLM2 performs the best in guiding patients to the correct department (Accuracy).
We find LLMs occasionally hallucinate and incorrectly guide patients to non-existent departments.
Yi-Chat excels at following instructions, generating department names with high accuracy and minimal hallucinations. Among clinical diagnosis sub-tasks, InternLM2 leads in the PD task, demonstrating a strong ability to identify possible diseases based on initial symptoms.
Gemini-Pro performs the best in the DB task, effectively extracting supportive medical evidence and exhibiting good reliability in clinical settings. Yi-Chat performs the best in the DD task, effectively eliminating interference from similar conditions with fewer hallucinations. GPT-4 excels in the FD task, demonstrating superior judgment and diagnostic accuracy. Claude3 leads in the PT and TP tasks, showcasing its deep understanding and application of basic medical knowledge. On the imaging diagnosis task, WiNGPT2 is significantly ahead all other models, indicating strong capabilities in analyzing medical imaging reports and utilizing medical equipment information.

Overall Performance. InternLM2 achieves the top $\mathsf{DWR}$ of 14.91 and Avg. 51.98, indicating its superiority over other models across multiple departments with balanced performance. We hypothesize that the diverse pre-training data, thorough data filtering methods, and careful executions of pre-training, supervised fine-tuning, and alignments through a novel reinforcement learning [] may have contributed to the superior performance of InternLM2.
GPT-4 performs the best in simultaneously predicting the correct department and disease diagnosis ($\mathsf{CDR}$),
showing its ability to complete 33.27% of end-to-end medical consultations.
Moreover, GPT-4 demonstrates the effectiveness of its content quality and accuracy with a top acceptability score of 11.37.

## 5 ClinicalAgent: An End-to-End Clinical Agent Aligned with Real-World Multi-Departmental Clinical Diagnostic Practices

### 5.1 ClinicalAgent: Methodology

Our findings in Section 4.3,
shows obvious performance deficiencies when requiring a single model to play different departmental roles in highly specialized medical scenarios. We believe that setting specific agents for different departments, i.e., the agent for each department is powered by the LLM that performs best in the specific domain, aligns better with the real-world practices of human medical experts. Therefore, we propose ClinicalAgent, an End-to-End Clinical Agent Aligned with Real-World Multi-Departmental Clinical Diagnostic Practices, with its diagnostic process shown in Figure 1. ClinicalAgent operates in 6 stages, as follows. Details of the stages and the workflow algorithm can be found in Appendix A.3.

Departmental Guide: The patient $\mathcal{PA}$ presents a chief complaint $\mathcal{CC}$ to the patient navigator agent $\mathcal{PN}$. Based on $\mathcal{CC}$ and the prior knowledge $\mathcal{PK}$ of LLM rankings obtained in Table 2, agent $\mathcal{PN}$ arranges $\mathcal{N}$ clinicians from the $\mathcal{K}$ most relevant departments in the consultation room to prepare a preliminary consultation for $\mathcal{PA}$.
The purpose of this stage is to quickly and accurately identify the patient’s initial needs and arrange for the appropriate specialty medical team to diagnose.

Preliminary Consultation: The $\mathcal{PA}$ consults with multiple clinicians from different departments in the consultation room, such as pediatrics clinician powered by GPT-4, orthopedic clinician powered by InternLM2, and hematology clinician powered by Gemini-Pro. Each clinician makes a preliminary diagnosis $\mathcal{P}$ based on the $\mathcal{CC}$, $\mathcal{MH}$, and $\mathcal{PE}$, and decide on further laboratory tests and imageological examinations.

Laboratory Examination: According to the preliminary consultation advice, the $\mathcal{PA}$ proceeds to the biochemistry laboratory for a series of necessary examinations, such as blood tests and urine analysis. Subsequently, based on the laboratory report $\mathcal{LR}$ generated by the medical instruments, the biochemist provides the laboratory diagnostic results $\mathcal{LDR}$.

Imageological Examination:
According to the preliminary consultation advice, the $\mathcal{PA}$ undergoes necessary imageological examinations in the radiology room, such as X-rays, CT scans, or MRI. Subsequently, the radiologist provides an imaging diagnosis result $\mathcal{IDR}$ based on the radiological reports $\mathcal{IR}$ of these medical images.

Final Consultation: The $\mathcal{PA}$ returns to the consultation room and provides $\mathcal{LDR}$ and $\mathcal{IDR}$ to the medical team. Based on the newly acquired reference information, the medical team conducts a step-by-step analysis and ultimately provides results including $\mathcal{B}$, $\mathcal{D}$, $\mathcal{F}$, $\mathcal{G}$, and $\mathcal{T}$.

Medical Treatment: The $\mathcal{PA}$ receives corresponding treatment based on $\mathcal{G}$ and $\mathcal{T}$, including treatments conducted in the treatment room and medication dispensed in the pharmacy. Finally, the patient leaves the hospital after completing the treatment.

### 5.2 ClinicalAgent: Evaluations

**Table 3: Detailed performance of LLMs on the Departmental Guide task.**
| Table 3: Detailed performance of LLMs on the Departmental Guide task.<br>Model<br>Acc@1<br>Acc@3<br>Acc@5<br>DIFR-Q<br>DIFR-N<br>Avg.<br>Gemini-Pro<br>62.07<br>85.73<br>92.00<br>100.0<br>88.90<br>85.74<br>Yi-Chat<br>58.80<br>83.60<br>86.07<br>99.93<br>96.23<br>84.93<br>InternLM2<br>64.47<br>79.87<br>84.67<br>100.0<br>94.37<br>84.68<br>HuatuoGPT2<br>59.20<br>80.47<br>88.87<br>100.0<br>94.73<br>84.65<br>GPT-4<br>61.47<br>86.13<br>90.13<br>100.0<br>74.03<br>82.35<br>Claude3<br>58.93<br>79.33<br>85.67<br>100.0<br>84.93<br>81.77<br>GPT-3.5<br>56.80<br>81.13<br>89.00<br>100.0<br>79.00<br>81.87<br>Qwen<br>63.67<br>80.73<br>86.67<br>100.0<br>70.70<br>80.35 | Table 4: Performance of ClinicalAgent and top-performing LLMs using three evaluation methods.<br>Model<br>Automatic Score<br>Human<br>GPT-4o<br>DWR<br>CDR<br>Acceptability<br>Avg.<br>Score<br>Score<br>InternLM2<br>14.91<br>31.40<br>11.11<br>51.98<br>54.66<br>85.09<br>GPT-4<br>12.70<br>33.27<br>11.37<br>49.45<br>55.90<br>90.93<br>Gemini-Pro<br>14.48<br>30.13<br>10.85<br>50.63<br>58.84<br>79.61<br>Yi-Chat<br>14.30<br>26.13<br>9.25<br>50.53<br>52.42<br>86.61<br>\hdashlineAgent#3@1<br>14.30<br>52.73<br>17.82<br>54.46<br>59.42<br>85.61<br>Agent#1@3<br>14.30<br>54.00<br>18.22<br>51.66<br>62.76<br>89.40<br>Agent#1@1<br>17.00<br>35.13<br>12.57<br>53.02<br>57.84<br>82.46 |
| --- | --- |

##### Experimental Setup

As described in Section 2.2, existing medical agents such as MedAgent [] only support multiple-choice questions and do not support end-to-end diagnosis. Therefore, we compare the default configuration of ClinicalAgent with several variants and the top-performing LLMs in Table 2 to verify the effectiveness of the ClinicalAgent approach. Each variant, Agent#K@N, employs different department scheduling and clinician allocation strategies for clinical diagnosis, where K denotes the number of departments to be scheduled, and N denotes the number of the top clinicians assigned to each department. For example,
Agent#1@3 schedules three top clinicians from the same department for diagnosis. We use three evaluation methods to measure the quality and accuracy of the agent’s diagnostic results: automatic evaluation, human evaluation, and GPT-4o evaluation. A detailed description of the three evaluation methods can be found in Appendix A.4.

According to modern medical practice, accurately guiding patients to the most appropriate departments is a crucial initial step in ClinicalAgent. Therefore, we thoroughly evaluate the patient navigation capabilities of various LLMs to select the best patient navigator. As shown in Table 4, despite the fact that Yi-Chat yields slightly lower Acc@1 than other models, we choose it as the patient navigator due to its near-perfect instruction following performance and minimal hallucinations. Detailed analysis and explanation of the reasons can be found in Appendix A.5.

##### Results & Analysis

Table 4 and Figure 6 illustrate the performance of 4 LLMs and 3 configurations of ClinicalAgent using the three different evaluation methods.
Tables 10-31 in the Appendix provide detailed case studies, including case information, diagnostic results from human doctors and seven models.
Using both Automatic Evaluation and Human Evaluation methods, ClinicalAgent (Agent#K@N) based on department scheduling and doctor allocation strategies leads top-performing LLMs with a large margin.
With GPT-4o evaluation, GPT-4 achieves the highest score of 90.93, which is probably attributable to the evaluation model’s preference for its own generated answers []. Even so, ClinicalAgent (Agent#K@N) receives competitive GPT-4o scores up to 89.40.
Overall, ClinicalAgent (Agent#K@N) excel in handling complex diagnosis tasks, showcasing the effectiveness of collaborative diagnostic strategies across multiple departments and doctors.

Figure: Figure 5: Performance trends of ClinicalAgent and LLMs using different evaluation metrics.
Refer to caption: x5.png

Notably, as shown in Figure 6, the trends based on different metrics do not completely align. This discrepancy arises from the varying emphases of each metric and highlights the limitations of existing metrics, demanding future studies.

## 6 Conclusion

We introduce ClinicalLab, which provides a comprehensive set of resources, tools, and foundational design methodologies for medical agents, offering crucial support for evaluating and designing future LLMs and medical agents.

## 7 Limitations

To date, no medical agents have been evaluated on real clinical data, including the recent MedAgents [] and Agent Hospital []. Existing agents are primarily designed for handling multiple-choice questions and are not suitable for simulating complex real-world scenarios. Specifically, after carefully reviewing the original paper and code of MedAgents, we find that evaluating it would require significant modifications to its experimental design and prompts. These changes would force MedAgents’ core process, which is designed for multiple-choice questions, to shift toward a generative question-answering approach, likely impacting its performance negatively. In addition to the previously mentioned differences in design goals, data incompatibility, and variations in evaluation metrics and scope, we are particularly concerned that such comparisons may lead to an unfair evaluation of MedAgents. Therefore, ClinicalAgent cannot be directly compared with other agents. However, as a foundational approach, ClinicalAgent can integrate well with other agents.

We have not conducted practical engineering tests on widely recognized methods such as few-shot learning and RAG, although these methods have proven effective in other medical agents. Future research can consider combining these mature techniques with our ClinicalAgent approach to explore their potential applications in clinical settings. We view this as a direction for future work.

The data in ClinicalBench comes from mainland China and only follows the officially recommended diagnostic methods and procedures in mainland China []. Therefore, there may be a lack of representativeness for other regions and countries.

## 8 Ethical Statement

Given that our research involves real patient records and their associated diagnostic processes, ethical considerations are central to this study. To mitigate potential ethical and privacy risks, we take the following measures: First, we implement privacy protection measures for all medical records by removing patient identities, treatment area information, and other sensitive content from the data samples. Second, we conduct three rounds of independent and rigorous review of the dataset to ensure it does not contain any sensitive information. Additionally, our study has been approved by the medical ethics committee of the hospital providing the data samples and has been officially notarized by a notary institution.
Finally, we require all users of the dataset to agree to the ClinicalBench Usage and Data Distribution License Agreement before downloading, which includes a commitment to protect patient privacy and explicit provisions prohibiting the tracking or identifying of any personal information. Through these measures, we ensure the ethical and legal compliance of our research.

We can provide supporting documents and certification from medical institutions and notary organizations to demonstrate the legality and ethical approval of our data collection process. Since the review process is double-blind, we do not include these documents in the supplementary materials. If you need to review them, please contact us to obtain the relevant materials.

## Appendix A Appendix

### A.1 The Secure Distribution of ClinicalBench

To further strengthen data security, we adopt an application-based data distribution system. Applicants must review and agree to the terms and commit to maintaining confidentiality. Additionally, we utilize an automated algorithm to embed personalized watermarks in each dataset, based on the applicant’s information. This enables us to swiftly trace the source in the event of a data breach. These measures ensure that ClinicalBench can provide valuable data to the medical AI research community while safeguarding privacy.

As of September 30, 2024, we have successfully distributed ClinicalBench datasets 63 times to researchers from various countries and regions, including the United States, China, the United Kingdom, Germany, and Singapore. This reflects the positive impact ClinicalBench is already making in the AI research community and its contribution to global advancements in medical AI. We are confident that ClinicalBench will continue to play a pivotal role in driving progress in medical AI research.

### A.2 Evaluation Metrics

Accuracy indicates the proportion of samples correctly classified by the department to the total number of samples. BLEU [] measures the degree of overlap between the predicted output and the reference output, ranging from 1-gram to 4-gram. Rouge [] primarily evaluates based on the longest common subsequence found between the generated texts and the human-written reference ones. BertScore [] leverages a pre-trained BERT model and its deep contextual representations to gauge the semantic similarity between the generated and reference texts. In the experiment, we use the “bert-base-chinese”(^5^55[https://huggingface.co/google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese)) model along with the “jieba”(^6^66[https://pypi.org/project/jieba/](https://pypi.org/project/jieba/)) Chinese segmentation tool to ensure accurate word tokenization.

To ensure the accuracy of BLEU, ROUGE, and BertScore when evaluating medical synonyms, we implement several key steps. First, we compile a list of synonyms for 150 diseases from medical textbooks and online encyclopedias, encompassing a range of expression variations. To address discrepancies in terminology arising from differences in model training data, we also request that 17 models independently generate synonym lists for each disease. We then merge the synonyms collected from textbooks and encyclopedias with those generated by the models. This combined list is rigorously screened by three medical experts among the authors to produce a standardized synonym list.

Before calculating BLEU, ROUGE, and BertScore, we use this standardized list to align the model-generated terms with the ground truth, minimizing errors due to synonym variations and ensuring the accuracy of the metrics. Furthermore, when distributing the dataset, we include the synonym list to enable other researchers to replicate and validate our work.

### A.3 ClinicalAgent Algorithm

The workflow of the aforementioned ClinicalAgent is shown in Algorithm 1.

Departmental Guide: When patient $\mathcal{PA}$ enters the hospital, they submit a simple chief complaint $\mathcal{CC}$ to the patient navigator agent supported by Yi-Chat []. The navigator agent, based on the $\mathcal{CC}$ and prior knowledge $\mathcal{PK}$ from Table 2, dynamically arranges the most relevant $\mathcal{K}$ departments $\mathcal{DP}_{\mathit{k}}(k=1,2,\dots,\mathcal{K})$ according to the complexity of the $\mathcal{CC}$, and assigns $\mathcal{N}$ top clinicians $\mathcal{CL}_{\mathit{n}}^{\mathcal{DP}_{\mathit{k}}}(n=1,2,\dots,\mathcal{N})$ from each department $\mathcal{DP}_{\mathit{k}}$ to form the clinician team $\mathcal{CT}$. The $\mathcal{CT}$ prepares for an initial consultation with $\mathcal{PA}$ in the consultation room. The purpose of this stage is to quickly and accurately identify the patient’s initial needs and arrange the appropriate specialized medical team for diagnosis.

Preliminary Consultation: First, a lead clinician $\mathcal{CL}_{\mathit{chair}}$ is selected from the clinical team $\mathcal{CT}$ to coordinate and summarize the entire diagnostic process. In the consultation room, the $\mathcal{PA}$ meets simultaneously with multiple clinicians from various departments, such as a pediatrics clinician $\mathcal{CL}_{\mathit{GPT-4}}^{\mathit{Pediatrics}}$ powered by GPT-4, an orthopedic clinician $\mathcal{CL}_{\mathit{InternLM2}}^{\mathit{Orthopedics}}$ powered by InternLM2, and a hematology clinician $\mathcal{CL}_{\mathit{Gemini-Pro}}^{\mathit{Hematology}}$ powered by Gemini-Pro. Each clinician $\mathcal{CL}_{\mathit{i}}$ briefly reviews the patient’s chief complaint $\mathcal{CC}$, medical history $\mathcal{MH}$, and physical examination $\mathcal{PE}$, then independently formulates a preliminary diagnosis $\mathcal{P}_{\mathit{i}}$ and recommends any necessary laboratory tests $\mathcal{LTS}_{\mathit{i}}$ or imaging examinations $\mathcal{ITS}_{\mathit{i}}$. Finally, the $\mathcal{CL}_{\mathit{chair}}$ consolidates all evaluations, synthesizing the diagnostic hypotheses and test suggestions into a unified preliminary diagnosis $\mathcal{P}$, and finalizes the required laboratory tests $\mathcal{LTS}$ and imaging examinations $\mathcal{ITS}$ for $\mathcal{PA}$.

Laboratory Examination:
Based on the preliminary consultation advice, the $\mathcal{PA}$ proceeds to the biochemical testing room to undergo a series of necessary tests according to the $\mathcal{LTS}$, such as blood tests and urine analysis. Then, based on the laboratory reports $\mathcal{LR}_{\mathit{i}}$ generated by medical instruments, the biochemist provides the corresponding laboratory diagnostic results $\mathcal{LDR}_{\mathit{i}}$. Finally, the biochemist consolidates all $\mathcal{LDR}_{\mathit{i}}$ to form the final laboratory diagnostic result $\mathcal{LDR}$ and delivers it to the $\mathcal{PA}$.

Imageological Examination: Based on the preliminary consultation advice, the $\mathcal{PA}$ proceeds to the radiology room to undergo a series of necessary examinations according to the imaging test requirements $\mathcal{ITS}$, such as X-rays, CT scans, or MRI. Then, the radiologist provides the corresponding imaging diagnostic results $\mathcal{IDR}_{\mathit{i}}$ based on the textual reports $\mathcal{IR}_{\mathit{i}}$ of the medical images. Finally, the radiologist consolidates all $\mathcal{IDR}_{\mathit{i}}$ to form the final imaging diagnostic result $\mathcal{IDR}$ and delivers it to the $\mathcal{PA}$.

Final Consultation: The $\mathcal{PA}$ returns to the consultation room and provides the $\mathcal{CT}$ with the $\mathcal{LDR}$ and $\mathcal{IDR}$. The $\mathcal{CT}$ discusses the newly obtained $\mathcal{LDR}$ and $\mathcal{IDR}$, with each clinician $\mathcal{CL}_{\mathit{i}}$ sequentially providing the diagnostic basis $\mathcal{B}_{\mathit{i}}$, differential diagnosis $\mathcal{D}_{\mathit{i}}$, final diagnosis $\mathcal{F}_{\mathit{i}}$, principle of treatment $\mathcal{G}_{\mathit{i}}$, and treatment plan $\mathcal{T}_{\mathit{i}}$. Finally, the $\mathcal{CL}_{\mathit{chair}}$ consolidates all evaluations, taking into account the suggestions from all team members, and provides the final $\mathcal{B}$, $\mathcal{D}$, $\mathcal{F}$, $\mathcal{G}$, and $\mathcal{T}$.

Medical Treatment: The $\mathcal{PA}$ receives corresponding treatment based on $\mathcal{F}$ and $\mathcal{T}$, including treatments conducted in the treatment room and medication dispensed in the pharmacy. Finally, the $\mathcal{PA}$ leaves the hospital after completing the treatment.

Figure: Algorithm 1 ClinicalAgent Algorithm

### A.4 Evaluation Methods for ClinicalAgent

##### Automatic Evaluation

We continue to use the automatic metrics mentioned in Section 4.2 to evaluate the performance of ClinicalAgent, including $\mathsf{DWR}$, $\mathsf{CDR}$, $\mathsf{Acceptability}$, and $\mathsf{Avg.}$

##### Human Evaluation

To evaluate the quality and accuracy of the model’s diagnostic results, we hire seven medical experts from different departments, with an average of over ten years of clinical experience, to conduct a human evaluation experiments. We randomly select 20 data cases and provide the experts with diagnostic results from seven different scenarios: ClinicalAgent, two baseline variants, and the four LLMs that perform best in the automatic evaluation. Each diagnostic result is anonymized to ensure that the evaluators cannot identify the corresponding model. Additionally, each sample is graded by two different experts in a double-blind cross-evaluation setting. Following the setup of previous work [], we ask the evaluators to rate the diagnostic results on a scale of 1-5 in four dimensions: fluency, relevance, completeness, and proficiency in medicine. Detailed rubics are provided in Figure 7.

Figure: Figure 7: A survey questionnaire for human evaluation of model diagnostic quality, including rating criteria for four dimensions: fluency, relevance, completeness, and proficiency in medical knowledge.
Refer to caption: x7.png

##### GPT-4o Evaluation

We randomly select one data sample for each disease, totaling 150 cases. We use GPT-4o to score the diagnostic results from seven models mentioned in Section A.4, following the same rubics as human evaluation.

### A.5 Patient Navigator Settings

Following the experimental design in Section 5.2, we use the Acc@K metric to evaluate navigation accuracy, with K values set at 1, 3, and 5. Additionally, following the description in Section 4.2, we calculate the $\mathsf{DIFR\text{-}Q}$ and $\mathsf{DIFR\text{-}N}$ to evaluate instruction following during navigation.

InternLM2, Gemini-Pro, and GPT-4 exhibit outstanding accuracy. Although most models are nearly perfect in the $\mathsf{DIFR\text{-}Q}$, they perform poorly in the $\mathsf{DIFR\text{-}N}$, especially GPT-4, having a $\mathsf{DIFR\text{-}N}$ of only 74.03%. Overall, Gemini-Pro and Yi sexhibit remarkable performance. However, Gemini-Pro still has deficiencies in the correctness of department names. We expect the patient navigator to not only accurately guide patients to the appropriate departments but also to minimize errors in department names. Therefore, we choose Yi-Chat as the patient navigator for ClinicalAgent.

Table: Table 5: A data example from ClinicalBench.

Table: Table 6: A data example from ClinicalBench. (Cont. Table 5)

Table: Table 7: A data example from ClinicalBench. (Cont. Table 6)

Table: Table 8: A data example from ClinicalBench. (Cont. Table 7)

Table: Table 9: A data example from ClinicalBench. (Cont. Table 8)

Table: Table 10: A detailed case study on ClinicalBench.

Table: Table 11: A detailed case study on ClinicalBench, diagnosed by Agent#1@3.

Table: Table 12: A detailed case study on ClinicalBench, diagnosed by Agent#3@1.

Table: Table 13: A detailed case study on ClinicalBench, diagnosed by Agent#1@1.

Table: Table 14: A detailed case study on ClinicalBench, diagnosed by InternLM2.

Table: Table 15: A detailed case study on ClinicalBench, diagnosed by GPT-4.

Table: Table 16: A detailed case study on ClinicalBench, diagnosed by Gemini-Pro.

Table: Table 17: A detailed case study on ClinicalBench, diagnosed by Yi-34B-Chat.

Table: Table 18: A detailed case study on ClinicalBench.

Table: Table 19: A detailed case study on ClinicalBench, diagnosed by human doctor.

Table: Table 20: A detailed case study on ClinicalBench, diagnosed by Agent#1@3.

Table: Table 21: A detailed case study on ClinicalBench, diagnosed by Agent#1@3. (Cont. Table20)

Table: Table 22: A detailed case study on ClinicalBench, diagnosed by Agent#3@1.

Table: Table 23: A detailed case study on ClinicalBench, diagnosed by Agent#3@1. (Cont. Table22)

Table: Table 24: A detailed case study on ClinicalBench, diagnosed by Gemini-Pro.

Table: Table 25: A detailed case study on ClinicalBench, diagnosed by Agent#1@1.

Table: Table 26: A detailed case study on ClinicalBench, diagnosed by Agent#1@1. (Cont. Table25)

Table: Table 27: A detailed case study on ClinicalBench, diagnosed by GPT-4.

Table: Table 28: A detailed case study on ClinicalBench, diagnosed by InternLM2.

Table: Table 29: A detailed case study on ClinicalBench, diagnosed by InternLM2. (Cont. Table28)

Table: Table 30: A detailed case study on ClinicalBench, diagnosed by Yi-34B-Chat.

Table: Table 31: A detailed case study on ClinicalBench, diagnosed by Yi-34B-Chat. (Cont. Table30)

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 3
presentation: 2
questions: - Could you provide the prompt formulation for each of the 8 tasks? I cannot find how the prompts for evaluation are formulated for each task in the manuscript or Appendix.
- For Sections 3.4.1, 3.4.2, and 3.4.3, is the input information based on ground-truth data? Additionally, do these three major function groups operate independently of one another?
- The equation for DWR might be incorrect. It is intended to measure the results of each LLM, but the equation aggregates the scores of all LLMs.
- What are the values for K' in the DIFR metric used in this study? Also, in the CDR metric, what are the disease diagnoses mentioned in L360? Additionally, how is it determined whether the prediction for disease diagnosis is correct? Is it simply a strict match between the ground-truth and the generated text (followed by your postprocessing technique for medical synonyms)?
- For the automatic evaluation of ClinicalAgent, could you provide the full experimental results similar to Table 2, including each of the 8 tasks?
- For the human or GPT-4o evaluation, do you assess the intermediate results produced by the agent, such as laboratory examinations or a set of tests? This is important in relation to the end-to-end evaluation of clinical agents.
soundness: 2
strengths: - The new large-scale evaluation benchmark for LLMs in the medical domain could be highly beneficial to the research community.
- A variety of LLMs were evaluated in this paper, allowing researchers to observe trends and understand which models are operational in various tasks.
summary: The paper introduces ClinicalLab, a comprehensive suite for aligning clinical diagnosis agents, which includes ClinicalBench, ClinicalMetrics, and ClinicalAgent. ClinicalBench is aimed at providing an end-to-end evaluation benchmark, while ClinicalMetrics comprises four novel metrics designed to assess the effectiveness of medical agents and LLMs. Additionally, the authors present ClinicalAgent, a new clinical diagnostic agent developed in response to the absence of a single expert LLM.
weaknesses: - Although the ClinicalBench benchmark is described as an end-to-end clinical diagnostic evaluation benchmark for assessing medical agents and LLMs (as presented in the abstract, L23-L24), the paper does not demonstrate an end-to-end clinical diagnostic evaluation for each LLM, nor is it clear how this evaluation is conducted. Specifically, according to Sections 3 and 4, all LLMs are evaluated based on their performance in eight separate tasks (using ground-truth input), rather than in an end-to-end manner. In other words, this benchmark can be viewed as a collection of QA benchmarks (with defined inputs and outputs) rather than as a benchmark for an end-to-end agent capable of sequential decision-making.

- Some metrics are redundant or unclear. Instead of the DWR metric, simply using micro accuracy (across departments) may be a better metric to show absolute values and map rankings. Additionally, the DIFR metrics are more related to instruction-following ability rather than clinical performance metrics. Furthermore, during the ClinicalBench evaluation, only one department is relevant, so it is unclear why the DIFR metric holds importance in this benchmarking context.

- The evaluation mechanisms for ClinicalAgent (Section 5) and the LLMs (Sections 3 and 4) differ, meaning that only the end-to-end evaluation results are conducted with ClinicalAgent, not the 17 LLMs. Specifically, the LLMs in Table 4 are evaluated based on individual abilities and do not make sequential predictions (the GPT-4o score is the same in both Table 2 and Table 4).

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: See above.
soundness: 3
strengths: This paper addresses important limitations in existing medical AI benchmarks, which is a unique contribution. The dataset includes comprehensive coverage of medical departments and diseases. In addition, they propose novel metrics for evaluating clinical diagnostic capabilities. From the dataset and benchmark perspective, they leverage real-world clinical data in a private dataset, which is not present in previous datasets, as shown in Table 1. They also thoroughly evaluate multiple language models and propose an aligned clinical agent system. 
- The work addresses critical gaps in evaluating AI models for clinical applications, potentially improving patient care and medical decision-making.
- The benchmark and agent align closely with real-world clinical practices, making the research highly relevant to both AI and medical communities.
- The research demonstrates rigorous methodology in data collection, metric development, and model evaluation.
- The authors show strong attention to patient privacy and ethical use of medical data with sufficient materials support in the supplementary materials submission.
summary: This paper introduces ClinicalLab, a comprehensive suite for aligning and evaluating clinical diagnostic agents. The main contributions include: a) ClinicalBench: A new end-to-end, multi-departmental clinical diagnostic evaluation benchmark based on real cases, covering 24 departments and 150 diseases. b) ClinicalMetrics: Four novel metrics for evaluating the effectiveness of language models in clinical diagnostic tasks. c) ClinicalAgent: An end-to-end clinical agent designed to align with real-world clinical diagnostic practices. The authors evaluate 17 general and medical-domain language models on ClinicalBench and propose ClinicalAgent as an improved approach for clinical diagnostics.
weaknesses: -  To me ClinicalAgent works as a medical domain-specific LLM without planning or tool-use, instead of LLM-based agents (e.g., AgentMD, EHRAgent, etc.). 
- If it works as a multi-agent system, then it might need further comparisons with other medical agents like MedAgent, MDAgents, etc.
- The proposed ClinicalMetrics, such as CDR, Acceptability, and DWR, are essentially combinations of existing metrics and should not be considered novel. DIFR is better characterized as a metric assessing instruction-following capabilities. Furthermore, BLEU, ROUGE, and BERTScore are not optimal for evaluating medical generative question-answering tasks, as medical cases often contain numerous abbreviations that can interfere with these metrics’ effectiveness.

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 2
questions: 1. Given the specific focus on dataset development and evaluation metrics, could this paper be more appropriately situated for publication in specialized journals dedicated to medical informatics or clinical data analysis?
soundness: 2
strengths: 1. This paper addresses a significant and timely topic by focusing on the performance evaluation of large language models (LLMs) in clinical diagnosis within the medical field. The relevance of this focus is crucial, given the increasing adoption of AI technologies in healthcare.
2. The authors have constructed a valuable real-world dataset that encompasses 24 departments and 150 diseases. This comprehensive dataset is a substantial asset for advancing the development of intelligent solutions in the medical domain, providing a solid foundation for future research and application in clinical diagnostics.
summary: This paper presents ClinicalLab, which comprises three key components: 1) ClinicalBench, an end-to-end multi-departmental clinical diagnostic evaluation benchmark designed for assessing medical agents and large language models (LLMs). ClinicalBench is based on real case data covering 24 departments and 150 diseases, ensuring no data leakage. 2) ClinicalMetrics, a set of metrics developed to evaluate the effectiveness of LLMs in clinical diagnostic tasks. 3) ClinicalAgent, an end-to-end clinical agent aligned with real-world clinical diagnostic practices.
weaknesses: 1. This paper addresses important challenges related to medical clinical datasets and evaluation metrics for auxiliary diagnostic tasks, which is a significant contribution to the field. However, it appears that the proposed solutions, particularly ClinicalMetrics, do not introduce substantial technical innovation or new perspectives in evaluation. The lack of novel insights raises questions about the overall advancement of the metrics presented. Further exploration of innovative approaches would enhance the contribution of this work to the literature.
2. The creation of a comprehensive real-world dataset encompassing 24 departments and 150 diseases is a notable contribution of this work. However, I would like to understand whether there are plans to release this dataset to the public in the future. If public access is not currently feasible, could the authors provide specific reasons for this limitation, such as privacy concerns or data governance issues?

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 2
questions: 1. It is mentioned that “LLMs still struggle to meet the strict requirements for accuracy and reliability in the medical field and face many challenges in clinical applications” ===> However, the current work does not any proof to prove that it can deal with this challenge?

Please provide specific evidence or comparative results demonstrating how the approach improves accuracy and reliability compared to existing benchmarks or LLMs in medical applications.

2. It is mentioned that “existing medical evaluation benchmarks face the risk of data leakage or contamination. And “We ensure that ClinicalBench does not have data leakage” ===> The reader can not find the detailed information to convince the claims from the author.

Please describe your methods to prevent data leakage, such as data collection procedures, preprocessing steps, or validation techniques used.

3. Existing evaluation methods are limited to multiple choice questions, which do not align with the real-world diagnostic scenarios ===> Propose Generative QA. The reviewer is not sure of the reliability of the questions; how are the composed validated sets validated by the healthcare professional practitioner? How can we follow this strategy and prepare for our in-hospital domain dataset?

4. The data processing, is not sufficient enough ===> How to deal with the preprocessing to have a complete structured data for the training and inference? For example, how to deal with numeric attributes from inside the notes?

5. Lacking of the demographic statistical analysis of the data (sex, age, etc.) Therefore, we are not certain of the generalization of the benchmark for the generated output from the agent? Especially, with the number of 1500 samples, it is actually not a large enough dataset, so we need to have more detailed how the dataset covers patient demographic statistic?

Please include a detailed demographic breakdown of their dataset, including age ranges, gender distribution, and other relevant factors. Additionally, please discuss how you ensured adequate representation across different demographic groups given the relatively small sample size.

6. We should expect to have the comparative analysis between the proposed benchmark, and the existing bechmarks from the literature? If not, it is not convinced to confirm the effectiveness of the proposed approach?

7. The most critical is the experiment setup in detailed so that the reproducibility can be made? Hyperparamters, fine-tuning approaches, etc….?

Please provide a detailed appendix or supplementary material containing complete hyperparameter settings, specific fine-tuning procedures, data preprocessing techniques, evaluation metric implementations, and code or pseudocode for key algorithms.

8. The experimental evaluation is completed through API calls and 8 NVIDIA A6000 GPUs ====> Should we have a table that compare the computational resource for the training between the proposed benchmark with different LLM models versus with existing benchmarks? Based on that, the interested readers should expect to estimated how much computational resource (training times, inference times, FLOPs, etc..) before they want to replicate the benchmarks and selected the LLM models in advance.

Please Include a comparative table showing computational resources (e.g., training times, inference times, FLOPs) for their benchmark across different LLM models and for existing benchmarks. This would help readers estimate resource requirements for replication or model selection.
soundness: 2
strengths: One of the strongest points is that the benchmark was designed based on several departments and diseases. 
Another point is the ClinicalAgent can perform an end-to-end for real-world clinical diagnostic practices.
summary: Thank you for your effort to provide comprehensive benchmarks for clinical practice. One of the strongest points is that the benchmark was designed based on several departments and diseases.  Another point is the ClinicalAgent can perform an end-to-end for real-world clinical diagnostic practices. 

However, the reviewer found major concerns in the manuscript. Those concerns concern the proof that the study's claims are not convinced, the reproducibility of the proposed benchmark, and the datasets.
weaknesses: proof that the study's claims are not sufficient, 
the reproducibility of the proposed benchmark, 
and the datasets.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 4
questions: - How are hallucinations quantified? Is it equivalent to the incorrect responses?
- Why do you think different LLMs excel at different tasks? How can LLMs improve upon their defeciencies?
- which internLM model was used? 1.8B? 7B? 20B? This is surprising that a small LLM is beating large ones.
- I'm curious to know how diversity was measured in a Chinese based health system? Alot of concerns in healthcare revolve around biases towards underrepresented communities. In the future, some racial, age, sex biases could elicit some exciting results.
- Another interesting direction is to see if language poses any barrier in current state of the art performance? I wish in the paper, you spoke more about the subtle differences between using Chinese Based LLMs versus English.
soundness: 4
strengths: - The paper is well motivated.
- Table 1 is helpful in giving us an understanding of the current state of clinical benchmarks.
- Easy to read.
- Very comprehensive benchmarking dataset.
summary: Paper provides an evaluation framework called ClinicalBench, ClinicalLab, and Clinical Agent meant for evaluating LLMs on robust, multidepartment, health agents. In ClinicalBench, the authors introduce the first real-case-based, data-leakage-free, end-to-end multidepartmental benchmark for evaluating the diagnostic capabilities of LLMs across 24 departments and 150 disease types, along with four novel ClinicalMetrics to assess the practicality and effectiveness of LLM outputs, and propose ClinicalAgent, a diagnostic agent designed for modern medical settings that outperforms competitive LLMs on this benchmark.
weaknesses: - The # of samples appears to be on the smaller end.
- diversity in the dataset was not well described in the main paper.