RadEval 0.1.2

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

RadEval

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

TL;DR

pip install -e .

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))

{
  "radgraph_simple": 0.72,
  "radgraph_partial": 0.61,
  "radgraph_complete": 0.61,
  "bleu": 0.36
}

Installation

pip install RadEval              # from PyPI
pip install RadEval[api]         # include OpenAI/Gemini for MammoGREEN

Or install from source:

git clone https://github.com/jbdel/RadEval.git && cd RadEval
conda create -n radeval python=3.11 -y && conda activate radeval
pip install -e '.[api]'

Supported Metrics

Category	Metric	Flag	Best For
Lexical	BLEU	do_bleu	Surface-level n-gram overlap
	ROUGE	do_rouge	Content coverage
Semantic	BERTScore	do_bertscore	Semantic similarity
	RadEval BERTScore	do_radeval_bertscore	Domain-adapted radiology semantics
Clinical	F1CheXbert	do_chexbert	CheXpert finding classification
	F1RadBERT-CT	do_f1radbert_ct	CT finding classification
	F1RadGraph	do_radgraph	Clinical entity/relation accuracy
	RaTEScore	do_ratescore	Entity-level synonym-aware scoring
Specialized	RadGraph-RadCliQ	do_radgraph_radcliq	Per-pair entity+relation F1 (RadCliQ variant)
	RadCliQ-v1	do_radcliq	Composite clinical relevance
	SRR-BERT	do_srr_bert	Structured report evaluation
	Temporal F1	do_temporal	Temporal consistency
	GREEN	do_green	LLM-based overall quality (7B model)
	MammoGREEN	do_mammo_green	Mammography-specific LLM scoring
	RadFact-CT	do_radfact_ct	LLM-based factual precision/recall for CT

Enable only the metrics you need -- each one is loaded lazily.

Detailed Output

Pass do_details=True to get per-sample scores, label breakdowns, and entity annotations for every enabled metric. See docs/metrics.md for the full output schema of each metric.

Comparing Systems

Use compare_systems to run paired approximate randomization tests between any number of systems:

from RadEval import RadEval, compare_systems

evaluator = RadEval(do_bleu=True)
signatures, scores = compare_systems(
    systems={
        'baseline': baseline_reports,
        'improved': improved_reports,
    },
    metrics={'bleu': lambda hyps, refs: evaluator(refs, hyps)['bleu']},
    references=reference_reports,
    n_samples=10000,
)

See docs/hypothesis_testing.md for a full walkthrough and interpretation guide.

Documentation

Page	Contents
docs/metrics.md	What each metric measures, do_details output schemas
docs/configuration.md	Full parameter reference, example presets
docs/hypothesis_testing.md	Statistical background, full example, performance notes
docs/file_formats.md	Loading data from .tok, .json, and Python lists

RadEval Expert Dataset

A curated evaluation set annotated by board-certified radiologists for validating automatic metrics. Available on HuggingFace.

Citation

@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",
    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",
}

Contributors

Jean-Benoit Delbrouck

Justin Xu

Xi Zhang

Acknowledgments

Built on the work of the radiology AI community: CheXbert, RadGraph, BERTScore, RaTEScore, SRR-BERT, GREEN, and datasets like MIMIC-CXR.

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