# RadEval

<div align="center">

**All-in-one metrics for evaluating AI-generated radiology text**

</div>

<!--- BADGES: START --->
[![PyPI](https://img.shields.io/badge/RadEval-v0.0.6-00B7EB?logo=python&logoColor=00B7EB)](https://pypi.org/project/RadEval/)
[![Python version](https://img.shields.io/badge/python-3.10+-important?logo=python&logoColor=important)]()
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### TL;DR
```
pip install -e .
```
```python
from RadEval import RadEval
import json

refs = [
    "Mild cardiomegaly with small bilateral pleural effusions and basilar atelectasis.",
    "No pleural effusions or pneumothoraces.",
]
hyps = [
    "Mildly enlarged cardiac silhouette with small pleural effusions and dependent bibasilar atelectasis.",
    "No pleural effusions or pneumothoraces.",
]

evaluator = RadEval(
    do_radgraph=True,
    do_bleu=True
)

results = evaluator(refs=refs, hyps=hyps)
print(json.dumps(results, indent=2))
```
```json
{
  "radgraph_simple": 0.72,
  "radgraph_partial": 0.61,
  "radgraph_complete": 0.61,
  "bleu": 0.36
}
```
With GREEN:

```python
export CUDA_VISIBLE_DEVICES=0,1 # define your available gpus for faster processing
[..]
evaluator = RadEval(
    do_radgraph=True,
    do_bleu=True,
    do_green=True
)

results = evaluator(refs=refs, hyps=hyps)
print(json.dumps(results, indent=2))
```
```json
(GREEN) Multi-GPU inference across 2 GPUs
Loading checkpoint shards: 100%|████████████████████████████████████████████████| 3/3 [00:02<00:00,  1.46it/s]
Loading checkpoint shards: 100%|████████████████████████████████████████████████| 3/3 [00:02<00:00,  1.45it/s]
Generating: 100%|███████████████████████████████████████████████████████████████| 2/2 [00:25<00:00, 12.61s/ex]

{
  "radgraph_simple": 0.72,
  "radgraph_partial": 0.61,
  "radgraph_complete": 0.61,
  "bleu": 0.36,
  "green": 0.875
}
```
```bibtex
@inproceedings{xu-etal-2025-radeval,
    title = "{R}ad{E}val: A framework for radiology text evaluation",
    author = "Xu, Justin  and
      Zhang, Xi  and
      Abderezaei, Javid  and
      Bauml, Julie  and
      Boodoo, Roger  and
      Haghighi, Fatemeh  and
      Ganjizadeh, Ali  and
      Brattain, Eric  and
      Van Veen, Dave  and
      Meng, Zaiqiao  and
      Eyre, David W  and
      Delbrouck, Jean-Benoit",
    editor = {Habernal, Ivan  and
      Schulam, Peter  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-demos.40/",
    doi = "10.18653/v1/2025.emnlp-demos.40",
    pages = "546--557",
    ISBN = "979-8-89176-334-0",
    abstract = "We introduce RadEval, a unified, open-source framework for evaluating radiology texts. RadEval consolidates a diverse range of metrics - from classic n{-}gram overlap (BLEU, ROUGE) and contextual measures (BERTScore) to clinical concept-based scores (F1CheXbert, F1RadGraph, RaTEScore, SRR-BERT, TemporalEntityF1) and advanced LLM{-}based evaluators (GREEN). We refine and standardize implementations, extend GREEN to support multiple imaging modalities with a more lightweight model, and pretrain a domain-specific radiology encoder - demonstrating strong zero-shot retrieval performance. We also release a richly annotated expert dataset with over 450 clinically significant error labels and show how different metrics correlate with radiologist judgment. Finally, RadEval provides statistical testing tools and baseline model evaluations across multiple publicly available datasets, facilitating reproducibility and robust benchmarking in radiology report generation."
}
```


## 📖 Table of Contents

- [🌟 Overview](#-overview)
  - [❓ Why RadEval](#-why-radeval)
  - [✨ Key Features](#-key-features)
- [⚙️ Installation](#️-installation)
- [🚀 Quick Start](#-quick-start)
- [📊 Evaluation Metrics](#-evaluation-metrics)
- [🔧 Configuration Options](#-configuration-options)
- [📁 File Format Suggestion](#-file-format-suggestion)
- [🧪 Hypothesis Testing (Significance Evaluation)](#-hypothesis-testing-significance-evaluation)
- [🧠 RadEval Expert Dataset](#-radeval-expert-dataset)
- [🚦 Performance Tips](#-performance-tips)
- [📚 Citation](#-citation)



## 🌟 Overview

**RadEval** is a comprehensive evaluation framework specifically designed for assessing the quality of AI-generated radiology text. It provides a unified interface to multiple state-of-the-art evaluation metrics, enabling researchers and practitioners to thoroughly evaluate their radiology text generation models.

### ❓ Why RadEval
> [!TIP]
> - **Domain-Specific**: Tailored for radiology text evaluation with medical knowledge integration
> - **Multi-Metric**: Supports 11+ different evaluation metrics in one framework
> - **Easy to Use**: Simple API with flexible configuration options
> - **Comprehensive**: From traditional n-gram metrics to advanced LLM-based evaluations
> - **Research-Ready**: Built for reproducible evaluation in radiology AI research

### ✨ Key Features
> [!NOTE]
> - **Multiple Evaluation Perspectives**: Lexical, semantic, clinical, and temporal evaluations
> - **Statistical Testing**: Built-in hypothesis testing for system comparison
> - **Batch Processing**: Efficient evaluation of large datasets
> - **Flexible Configuration**: Enable/disable specific metrics based on your needs
> - **Detailed Results**: Comprehensive output with metric explanations
> - **File Format Support**: Direct evaluation from common file formats (.tok, .txt, .json)

## ⚙️ Installation
RadEval supports Python **3.10+** and can be installed via PyPI or from source.

### Option 1: Install via PyPI (Recommended)

```bash
pip install RadEval
```
> [!TIP]
> We recommend using a virtual environment to avoid dependency conflicts, especially since some metrics require loading large inference models.

### Option 2: Install from GitHub (Latest Development Version)
Install the most up-to-date version directly from GitHub:
```bash
pip install git+https://github.com/jbdel/RadEval.git
```
> This is useful if you want the latest features or bug fixes before the next PyPI release.

### Option 3: Install in Development Mode (Recommended for Contributors)
```bash
# Clone the repository
git clone https://github.com/jbdel/RadEval.git
cd RadEval

# Create and activate a conda environment
conda create -n RadEval python=3.11 -y
conda activate RadEval

# Install in development (editable) mode
pip install -e .
```
> This setup allows you to modify the source code and reflect changes immediately without reinstallation.

## 🚀 Quick Start

### Example 1: Basic Evaluation
Evaluate a few reports using selected metrics:
```python
from RadEval import RadEval
import json

refs = [
    "Mild cardiomegaly with small bilateral pleural effusions and basilar atelectasis.",
    "No pleural effusions or pneumothoraces.",
]
hyps = [
    "Mildly enlarged cardiac silhouette with small pleural effusions and dependent bibasilar atelectasis.",
    "No pleural effusions or pneumothoraces.",
]

evaluator = RadEval(
    do_radgraph=True,
    do_bleu=True
)

results = evaluator(refs=refs, hyps=hyps)
print(json.dumps(results, indent=2))
```
<details>
<summary> Output </summary>

```json
{
  "radgraph_simple": 0.7222,
  "radgraph_partial": 0.6111,
  "radgraph_complete": 0.6111,
  "bleu": 0.3605
}
```

</details>

### Example 2: Comprehensive Evaluation
Set `do_details=True` to enable per-metric detailed outputs, including entity-level comparisons and score-specific breakdowns when supported.

```python
from RadEval import RadEval
import json

evaluator = RadEval(
    do_srr_bert=True,
    do_rouge=True,
    do_details=True
)

refs = [
    "Mild cardiomegaly with small bilateral pleural effusions and basilar atelectasis.",
    "No pleural effusions or pneumothoraces.",
]
hyps = [
    "Mildly enlarged cardiac silhouette with small pleural effusions and dependent bibasilar atelectasis.",
    "No pleural effusions or pneumothoraces.",
]

results = evaluator(refs=refs, hyps=hyps)
print(json.dumps(results, indent=2))
```

<details>
<summary> Output </summary>

```json
{
  "rouge": {
    "rouge1": {
      "mean_score": 0.7727272727272727,
      "sample_scores": [
        0.5454545454545454,
        1.0
      ]
    },
    "rouge2": {
      "mean_score": 0.65,
      "sample_scores": [
        0.3,
        1.0
      ]
    },
    "rougeL": {
      "mean_score": 0.7727272727272727,
      "sample_scores": [
        0.5454545454545454,
        1.0
      ]
    }
  },
  "srr_bert": {
    "srr_bert_weighted_f1": {
      "weighted_mean_score": 1.0,
      "sample_scores": [
        0.999999995,
        0.999999995
      ]
    },
    "srr_bert_weighted_precision": {
      "weighted_mean_score": 1.0,
      "sample_scores": [
        0.9999999900000002,
        0.9999999900000002
      ]
    },
    "srr_bert_weighted_recall": {
      "weighted_mean_score": 1.0,
      "sample_scores": [
        0.9999999900000002,
        0.9999999900000002
      ]
    },
    "label_scores": {
      "Atelectasis (Present)": {
        "f1-score": 1.0,
        "precision": 1.0,
        "recall": 1.0,
        "support": 1.0
      },
      "Simple pleural effusion (Present)": {
        "f1-score": 1.0,
        "precision": 1.0,
        "recall": 1.0,
        "support": 1.0
      },
      "Cardiomegaly (Present)": {
        "f1-score": 1.0,
        "precision": 1.0,
        "recall": 1.0,
        "support": 1.0
      },
      "No Finding": {
        "f1-score": 1.0,
        "precision": 1.0,
        "recall": 1.0,
        "support": 1.0
      }
    }
  }
}
```

</details>

### Example 3: Quick Hypothesis Testing
Compare two systems statistically to validate improvements:

```python
from RadEval import RadEval, compare_systems

# Define systems to compare
systems = {
    'baseline': [
        "No acute findings.",
        "Mild heart enlargement."
    ],
    'improved': [
        "No acute cardiopulmonary process.",
        "Mild cardiomegaly with clear lung fields."
    ]
}

# Reference ground truth
references = [
    "No acute cardiopulmonary process.",
    "Mild cardiomegaly with clear lung fields."
]

# Initialise evaluators only for selected metrics
bleu_evaluator = RadEval(do_bleu=True)
rouge_evaluator = RadEval(do_rouge=True)

# Wrap metrics into callable functions
metrics = {
    'bleu': lambda hyps, refs: bleu_evaluator(refs, hyps)['bleu'],
    'rouge1': lambda hyps, refs: rouge_evaluator(refs, hyps)['rouge1'],
}

# Run statistical test
signatures, scores = compare_systems(
    systems=systems,
    metrics=metrics, 
    references=references,
    n_samples=50,           # Number of bootstrap samples
    print_results=True      # Print significance table
)
```

<details>
<summary> Output </summary>

<pre lang="md">
================================================================================
PAIRED SIGNIFICANCE TEST RESULTS
================================================================================
System                                             bleu         rouge1
----------------------------------------------------------------------
Baseline: baseline                              0.0000         0.3968   
----------------------------------------------------------------------
improved                                      1.0000         1.0000   
                                           (p=0.4800)     (p=0.4600)  
----------------------------------------------------------------------
- Significance level: 0.05
- '*' indicates significant difference (p < significance level)
- Null hypothesis: systems are essentially the same
- Significant results suggest systems are meaningfully different

METRIC SIGNATURES:
- bleu: bleu|ar:50|seed:12345
- rouge1: rouge1|ar:50|seed:12345
</pre>

</details>

### Example 4: File-based Evaluation
Recommended for batch evaluation of large sets of generated reports.
```python
import json
from RadEval import RadEval

def evaluate_from_files():
    def read_reports(filepath):
        with open(filepath, 'r') as f:
            return [line.strip() for line in f if line.strip()]
    
    refs = read_reports('ground_truth.tok')
    hyps = read_reports('model_predictions.tok')
    
    evaluator = RadEval(
        do_radgraph=True,
        do_bleu=True,
        do_bertscore=True,
        do_chexbert=True
    )
    
    results = evaluator(refs=refs, hyps=hyps)
    
    with open('evaluation_results.json', 'w') as f:
        json.dump(results, f, indent=2)

    return results
```

## 📊 Evaluation Metrics

RadEval currently supports the following evaluation metrics:

| Category | Metric | Description | Best For |
|----------|--------|-------------|----------|
| **Lexical** | BLEU | N-gram overlap measurement | Surface-level similarity |
| | ROUGE | Recall-oriented evaluation | Content coverage |
| **Semantic** | BERTScore | BERT-based semantic similarity | Semantic meaning preservation |
| | RadEval BERTScore | Domain-adapted ModernBertModel evaluation | Medical text semantics |
| **Clinical** | CheXbert | Clinical finding classification | Medical accuracy |
| | RadGraph | Knowledge graph-based evaluation | Clinical relationship accuracy |
| | RaTEScore |  Entity-level assessments | Medical synonyms |
| **Specialized** | RadCLIQ | Composite multiple metrics | Clinical relevance |
| | SRR-BERT | Structured report evaluation | Report structure quality |
| | Temporal F1  | Time-sensitive evaluation | Temporal consistency |
| | GREEN | LLM-based metric | Overall radiology report quality |
| | Mammo Green | LLM-based metric | Mammography report quality |

## 🔧 Configuration Options

### RadEval Constructor Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `do_radgraph` | bool | False | Enable RadGraph evaluation |
| `do_green` | bool | False | Enable GREEN metric |
| `do_mammo_green` | bool | False | Enable Mammo Green metric |
| `do_bleu` | bool | False | Enable BLEU evaluation |
| `do_rouge` | bool | False | Enable ROUGE metrics |
| `do_bertscore` | bool | False | Enable BERTScore |
| `do_srr_bert` | bool | False | Enable SRR-BERT |
| `do_chexbert` | bool | False | Enable CheXbert classification |
| `do_temporal` | bool | False | Enable temporal evaluation |
| `do_ratescore` | bool | False | Enable RateScore |
| `do_radcliq` | bool | False | Enable RadCLIQ |
| `do_radeval_bertscore` | bool | False | Enable RadEval BERTScore |
| `do_details` | bool | False | Include detailed metrics |

### Example Configurations

```python
# Lightweight evaluation (fast)
light_evaluator = RadEval(
    do_bleu=True,
    do_rouge=True
)

# Medical focus (clinical accuracy)
medical_evaluator = RadEval(
    do_radgraph=True,
    do_chexbert=True,
    do_green=True
)

# Comprehensive evaluation (all metrics)
full_evaluator = RadEval(
    do_radgraph=True,
    do_green=True,
    do_bleu=True,
    do_rouge=True,
    do_bertscore=True,
    do_srr_bert=True,
    do_chexbert=True,
    do_temporal=True,
    do_ratescore=True,
    do_radcliq=True,
    do_radeval_bertscore=True,
    do_details=False           # Optional: return detailed metric breakdowns
)
```

## 📁 File Format Suggestion

To ensure efficient evaluation, we recommend formatting your data in one of the following ways:

### 📄 Text Files (.tok, .txt)
Each line contains one report
```
No acute cardiopulmonary process.
Mild cardiomegaly noted.
Normal chest radiograph.
```
Use two separate files:
> - ground_truth.tok — reference reports
> - model_predictions.tok — generated reports

### 🧾 JSON Files
```json
{
  "references": [
    "No acute cardiopulmonary process.",
    "Mild cardiomegaly noted."
  ],
  "hypotheses": [
    "Normal chest X-ray.",
    "Enlarged heart observed."
  ]
}
```

### 🐍 Python Lists
```python
refs = ["Report 1", "Report 2"]
hyps = ["Generated 1", "Generated 2"]
```
> [!TIP]
> File-based input is recommended for batch evaluation and reproducibility in research workflows.


## 🧪 Hypothesis Testing (Significance Evaluation)
RadEval supports **paired significance testing** to statistically compare different radiology report generation systems using **Approximate Randomization (AR)**.

This allows you to determine whether an observed improvement in metric scores is **statistically significant**, rather than due to chance.

### 📌 Key Features

- **Paired comparison** of any number of systems against a baseline
- **Statistical rigor** using Approximate Randomization (AR) testing
- **All built-in metrics** supported (BLEU, ROUGE, BERTScore, RadGraph, CheXbert, etc.)  
- **Custom metrics** integration for domain-specific evaluation
- **P-values** and significance markers (`*`) for easy interpretation

### 🧮 Statistical Background

The hypothesis testing uses **Approximate Randomization** to determine if observed metric differences are statistically significant:

1. **Null Hypothesis (H₀)**: The two systems perform equally well
2. **Test Statistic**: Difference in metric scores between systems
3. **Randomization**: Shuffle system assignments and recalculate differences
4. **P-value**: Proportion of random shuffles with differences ≥ observed
5. **Significance**: If p < 0.05, reject H₀ (systems are significantly different)

> [!NOTE]
> **Why AR testing?** 
> Unlike parametric tests, AR makes no assumptions about score distributions, making it ideal for evaluation metrics that may not follow normal distributions.

### 👀 Understanding the Results

**Interpreting P-values:**
- **p < 0.05**: Statistically significant difference (marked with `*`)
- **p ≥ 0.05**: No significant evidence of difference
- **Lower p-values**: Stronger evidence of real differences

**Practical Significance:**
- Look for consistent improvements across multiple metrics
- Consider domain relevance (e.g., RadGraph for clinical accuracy)  
- Balance statistical and clinical significance

### 🖇️ Example: Compare RadEval Default Metrics and a Custom Metric

#### Step 1: Initialize packages and dataset
```python
from RadEval import RadEval, compare_systems

# Reference ground truth reports
references = [
    "No acute cardiopulmonary process.",
    "No radiographic findings to suggest pneumonia.",
    "Mild cardiomegaly with clear lung fields.",
    "Small pleural effusion on the right side.",
    "Status post cardiac surgery with stable appearance.",
]
# Three systems: baseline, improved, and poor
systems = {
    'baseline': [
        "No acute findings.",
        "No pneumonia.",
        "Mild cardiomegaly, clear lungs.",
        "Small right pleural effusion.",
        "Post-cardiac surgery, stable."
    ],
    'improved': [
        "No acute cardiopulmonary process.",
        "No radiographic findings suggesting pneumonia.",
        "Mild cardiomegaly with clear lung fields bilaterally.",
        "Small pleural effusion present on the right side.",
        "Status post cardiac surgery with stable appearance."
    ],
    'poor': [
        "Normal.",
        "OK.",
        "Heart big.",
        "Some fluid.",
        "Surgery done."
    ]
}
```

#### Step 2: Define Evaluation Metrics and Parameters
We define each evaluation metric using a dedicated RadEval instance (configured to compute one specific score), and also include a simple custom metric — average word count. All metrics are wrapped into a unified metrics dictionary for flexible evaluation and comparison.

```python
# Initialise each evaluator with the corresponding metric
bleu_evaluator = RadEval(do_bleu=True)
rouge_evaluator = RadEval(do_rouge=True)
bertscore_evaluator = RadEval(do_bertscore=True)
radgraph_evaluator = RadEval(do_radgraph=True)
chexbert_evaluator = RadEval(do_chexbert=True)

# Define a custom metric: average word count of generated reports
def word_count_metric(hyps, refs):
    return sum(len(report.split()) for report in hyps) / len(hyps)

# Wrap metrics into a unified dictionary of callables
metrics = {
    'bleu': lambda hyps, refs: bleu_evaluator(refs, hyps)['bleu'],
    'rouge1': lambda hyps, refs: rouge_evaluator(refs, hyps)['rouge1'],
    'rouge2': lambda hyps, refs: rouge_evaluator(refs, hyps)['rouge2'],
    'rougeL': lambda hyps, refs: rouge_evaluator(refs, hyps)['rougeL'],
    'bertscore': lambda hyps, refs: bertscore_evaluator(refs, hyps)['bertscore'],
    'radgraph': lambda hyps, refs: radgraph_evaluator(refs, hyps)['radgraph_partial'],
    'chexbert': lambda hyps, refs: chexbert_evaluator(refs, hyps)['chexbert-5_macro avg_f1-score'],
    'word_count': word_count_metric  # ← example of a simple custom-defined metric
}
```

> [!TIP] 
> - Each metric function takes (hyps, refs) as input and returns a single float score.
> - This modular design allows you to flexibly plug in or remove metrics without changing the core logic of RadEval or compare_systems.
> - For advanced, you may define your own `RadEval(do_xxx=True)` variant or custom metrics and include them seamlessly here.

#### Step 3 Run significance testing

Use `compare_systems` to evaluate all defined systems against the reference reports using the metrics specified above. This step performs randomization-based significance testing to assess whether differences between systems are statistically meaningful.

```python
print("Running significance tests...")

signatures, scores = compare_systems(
    systems=systems,
    metrics=metrics,
    references=references,
    n_samples=50,                    # Number of randomization samples
    significance_level=0.05,         # Alpha level for significance testing
    print_results=True              # Print formatted results table
)
```

<details>
<summary> Output </summary>

<pre lang="md">
Running tests...
================================================================================
PAIRED SIGNIFICANCE TEST RESULTS
================================================================================
System                                             bleu         rouge1         rouge2         rougeL      bertscore       radgraph       chexbert     word_count
----------------------------------------------------------------------------------------------------------------------------------------------------------------
Baseline: baseline                              0.0000         0.6652         0.3133         0.6288         0.6881         0.5538         1.0000         3.2000   
----------------------------------------------------------------------------------------------------------------------------------------------------------------
improved                                      0.6874         0.9531         0.8690         0.9531         0.9642         0.9818         1.0000         6.2000   
                                           (p=0.0000)*    (p=0.0800)     (p=0.1200)     (p=0.0600)     (p=0.0400)*    (p=0.1200)     (p=1.0000)     (p=0.0600)  
----------------------------------------------------------------------------------------------------------------------------------------------------------------
poor                                          0.0000         0.0444         0.0000         0.0444         0.1276         0.0000         0.8000         1.6000   
                                           (p=0.4000)     (p=0.0400)*    (p=0.0600)     (p=0.1200)     (p=0.0400)*    (p=0.0200)*    (p=1.0000)     (p=0.0400)* 
----------------------------------------------------------------------------------------------------------------------------------------------------------------
- Significance level: 0.05
- '*' indicates significant difference (p < significance level)
- Null hypothesis: systems are essentially the same
- Significant results suggest systems are meaningfully different

METRIC SIGNATURES:
- bleu: bleu|ar:50|seed:12345
- rouge1: rouge1|ar:50|seed:12345
- rouge2: rouge2|ar:50|seed:12345
- rougeL: rougeL|ar:50|seed:12345
- bertscore: bertscore|ar:50|seed:12345
- radgraph: radgraph|ar:50|seed:12345
- chexbert: chexbert|ar:50|seed:12345
- word_count: word_count|ar:50|seed:12345
</pre>

</details>

> [!TIP]
> - The output includes mean scores for each metric and system, along with p-values comparing each system to the baseline.
> - Statistically significant improvements (or declines) are marked with an asterisk `*` if p < 0.05.
> - `signatures` stores each metric configuration (e.g. random seed, sample size), and `scores` contains raw score values per system for further analysis or plotting.

#### Step 4: Summarise Significant Findings

```python
# Significance testing
print("\nSignificant differences (p < 0.05):")
baseline_name = list(systems.keys())[0] # Assume first one is the baseline

for system_name in systems.keys():
    if system_name == baseline_name:
        continue
        
    significant_metrics = []
    for metric_name in metrics.keys():
        pvalue_key = f"{metric_name}_pvalue"
        if pvalue_key in scores[system_name]:
            p_val = scores[system_name][pvalue_key]
            if p_val < 0.05:
                significant_metrics.append(metric_name)
    
    if significant_metrics:
        print(f"  {system_name} vs {baseline_name}: {', '.join(significant_metrics)}")
    else:
        print(f"  {system_name} vs {baseline_name}: No significant differences")
```

<details>
<summary> Output </summary>

<pre lang="md">
Significant differences (p < 0.05):
  improved vs baseline: bleu, bertscore
  poor vs baseline: rouge1, bertscore, radgraph, word_count
</pre>

</details>

> [!TIP]
> This makes it easy to:
> - Verify whether model improvements are meaningful
> - Test new metrics or design your own
> - Report statistically sound results in your paper

## 🧠 RadEval Expert Dataset
To support reliable benchmarking, we introduce the **RadEval Expert Dataset**, a carefully curated evaluation set annotated by board-certified radiologists. This dataset consists of realistic radiology reports and challenging model generations, enabling nuanced evaluation across clinical accuracy, temporal consistency, and language quality. It serves as a gold standard to validate automatic metrics and model performance under expert review.

## 🚦 Performance Tips

1. **Start Small**: Test with a few examples before full evaluation
2. **Select Metrics**: Only enable metrics you actually need
3. **Batch Processing**: Process large datasets in smaller chunks
4. **GPU Usage**: Ensure CUDA is available for faster computation


## 📚 Citation

If you use RadEval in your research, please cite:

```BibTeX
@inproceedings{xu-etal-2025-radeval,
    title = "{R}ad{E}val: A framework for radiology text evaluation",
    author = "Xu, Justin  and
      Zhang, Xi  and
      Abderezaei, Javid  and
      Bauml, Julie  and
      Boodoo, Roger  and
      Haghighi, Fatemeh  and
      Ganjizadeh, Ali  and
      Brattain, Eric  and
      Van Veen, Dave  and
      Meng, Zaiqiao  and
      Eyre, David W  and
      Delbrouck, Jean-Benoit",
    editor = {Habernal, Ivan  and
      Schulam, Peter  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-demos.40/",
    doi = "10.18653/v1/2025.emnlp-demos.40",
    pages = "546--557",
    ISBN = "979-8-89176-334-0",
    abstract = "We introduce RadEval, a unified, open-source framework for evaluating radiology texts. RadEval consolidates a diverse range of metrics - from classic n{-}gram overlap (BLEU, ROUGE) and contextual measures (BERTScore) to clinical concept-based scores (F1CheXbert, F1RadGraph, RaTEScore, SRR-BERT, TemporalEntityF1) and advanced LLM{-}based evaluators (GREEN). We refine and standardize implementations, extend GREEN to support multiple imaging modalities with a more lightweight model, and pretrain a domain-specific radiology encoder - demonstrating strong zero-shot retrieval performance. We also release a richly annotated expert dataset with over 450 clinically significant error labels and show how different metrics correlate with radiologist judgment. Finally, RadEval provides statistical testing tools and baseline model evaluations across multiple publicly available datasets, facilitating reproducibility and robust benchmarking in radiology report generation."
}
```

### 📦 Codebase Contributors
<table>
  <tbody>
    <tr>
      <td align="center">
        <a href="https://jbdel.github.io/">
          <img src="https://aimi.stanford.edu/sites/g/files/sbiybj20451/files/styles/medium_square/public/media/image/image5_0.png?h=f4e62a0a&itok=euaj9VoF"
               width="100" height="100"
               style="object-fit: cover; border-radius: 20%;" alt="Jean-Benoit Delbrouck"/>
          <br />
          <sub><b>Jean-Benoit Delbrouck</b></sub>
        </a>
      </td>
      <td align="center">
        <a href="https://justin13601.github.io/">
          <img src="https://justin13601.github.io/images/pfp2.JPG"
               width="100" height="100"
               style="object-fit: cover; border-radius: 20%;" alt="Justin Xu"/>
          <br />
          <sub><b>Justin Xu</b></sub>
        </a>
      </td>
      <td align="center">
        <a href="https://x-izhang.github.io/">
          <img src="https://x-izhang.github.io/author/xi-zhang/avatar_hu13660783057866068725.jpg"
               width="100" height="100"
               style="object-fit: cover; border-radius: 20%;" alt="Xi Zhang"/>
          <br />
          <sub><b>Xi Zhang</b></sub>
        </a>
      </td>
    </tr>
  </tbody>
</table>

## 🙏 Acknowledgments

This project would not be possible without the foundational work of the radiology AI community.  
We extend our gratitude to the authors and maintainers of the following open-source projects and metrics:

- 🧠 **CheXbert**, **RadGraph**, and **CheXpert** from Stanford AIMI for their powerful labelers and benchmarks.
- 📐 **BERTScore** and **BLEU/ROUGE** for general-purpose NLP evaluation.
- 🏥 **RadCliQ** and **RaTE Score** for clinically grounded evaluation of radiology reports.
- 🧪 **SRR-BERT** for structured report understanding in radiology.
- 🔍 Researchers contributing to temporal and factual consistency metrics in medical imaging.

Special thanks to:
- All contributors to open datasets such as **MIMIC-CXR**, which make reproducible research possible.
- Our collaborators for their support and inspiration throughout development.

We aim to build on these contributions and promote accessible, fair, and robust evaluation of AI-generated radiology text.


---

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