Retinal Finding and Diagnosis Consistency Evaluation Dataset
- Records
- 5840
- Size
- 0.3GB
- Format
- JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-04
This evaluation dataset provides JSON records of structured clinical findings and diagnoses related to the retina, macula, and vitreous, with labels for consistency, conflict type, and explanation. It requires no image files as input and supports evaluation of a model's ability to detect contradictions within clinical records, as well as ophthalmic text quality control and model validation.
Overview
Dataset Insights
Preview
Sample Examples
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| conflict_type | string | Categorizes the conflict between findings and diagnosis; use none when they are consistent. |
| is_consistent | boolean | Indicates whether the documented diagnosis is consistent with the structured clinical findings. |
| clinical_record | object | Structured clinical record containing retinal findings and diagnosis information. |
| conflict_explanation | string | Briefly explains the mismatch between findings and diagnosis, or why they are consistent. |
Trust
Compliance Statement
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use
Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization
No PII, no real company names, simulated scenarios follow industry standards
Compliance System
Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs
FAQ
Frequently Asked Questions
- What does the dataset contain?
- Each record includes structured clinical findings and diagnosis information, along with a consistency label, conflict type, and brief explanation. The records focus on the retina, macula, and vitreous.
- Are retinal images required for evaluation?
- No. The benchmark uses structured clinical text in JSON format as input, making it suitable for evaluating whether models can judge logical consistency between documented findings and diagnoses.
- Which tasks is the dataset suitable for?
- It can support clinical record consistency checking, diagnosis contradiction detection, conflict type classification, and ophthalmic text quality control. It can also serve as an automated evaluation benchmark for medical language models.
- How can I access the data, and what are the licensing terms?
- Check the dataset product page for available access methods, licensing scope, and usage restrictions. For clinical or research projects, follow the stated license terms and applicable privacy requirements.
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Cite this Work
@dataset{Mobiusi2026,
title={Retinal Finding and Diagnosis Consistency Evaluation Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/fb09b6d4630793f362774f60b870f788?dataset_scene_cate_type=3},
urldate={2026-10-22},
keywords={retinal dataset,ophthalmic clinical records,diagnosis consistency benchmark,retinal finding analysis,medical text quality control,diagnosis contradiction detection,macular record data,vitreous clinical data},
version={1.0}
}Using this in research? Please cite us.