# PARROT: An Open Multilingual Radiology Reports Dataset

arXiv: https://arxiv.org/abs/2507.22939 · HTML: https://mlsanity.com/paper/2507.22939/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 2 of 2)

- [Bias in Large Language Models Across Clinical Applications: A Systematic Review](https://mlsanity.com/paper/2504.02917/) (2025)
- [ReXamine-Global: A Framework for Uncovering Inconsistencies in Radiology Report Generation Metrics](https://mlsanity.com/paper/2408.16208/) (2024)

## Built on by (most cited first, 2 of 2)

- [MedTri: A Platform for Structured Medical Report Normalization to Enhance Vision-Language Pretraining](https://mlsanity.com/paper/2602.22143/) (2026)
- [PARHAF, a human-authored corpus of clinical reports for fictitious patients in French](https://mlsanity.com/paper/2603.20494/) (2026)

## Abstract (arXiv)

Rationale and Objectives: To develop and validate PARROT (Polyglottal Annotated Radiology Reports for Open Testing), a large, multicentric, open-access dataset of fictional radiology reports spanning multiple languages for testing natural language processing applications in radiology. Materials and Methods: From May to September 2024, radiologists were invited to contribute fictional radiology reports following their standard reporting practices. Contributors provided at least 20 reports with associated metadata including anatomical region, imaging modality, clinical context, and for non-English reports, English translations. All reports were assigned ICD-10 codes. A human vs. AI report differentiation study was conducted with 154 participants (radiologists, healthcare professionals, and non-healthcare professionals) assessing whether reports were human-authored or AI-generated. Results: The dataset comprises 2,658 radiology reports from 76 authors across 21 countries and 13 languages. Reports cover multiple imaging modalities (CT: 36.1%, MRI: 22.8%, radiography: 19.0%, ultrasound: 16.8%) and anatomical regions, with chest (19.9%), abdomen (18.6%), head (17.3%), and pelvis (14.1%) being most prevalent. In the differentiation study, participants achieved 53.9% accuracy (95% CI: 50.7%-57.1%) in distinguishing between human and AI-generated reports, with radiologists performing significantly better (56.9%, 95% CI: 53.3%-60.6%, p<0.05) than other groups. Conclusion: PARROT represents the largest open multilingual radiology report dataset, enabling development and validation of natural language processing applications across linguistic, geographic, and clinical boundaries without privacy constraints.