Neuro-Ophthalmology Diagnostic Criteria Constraint Verification Benchmark
#Diagnostic criteria verification #Constraint checking #Missing evidence identification #Contradiction detection #Neuro-ophthalmology evaluation #Clinical rule reasoning #Medical AI validation #Structured case analysis
- 3340
- 0.2GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-11-04
AI Analysis & Value Prop
This structured JSON benchmark contains neuro-ophthalmology case information, examination findings, candidate diagnoses, and corresponding diagnostic criteria, with tasks that check case evidence against each criterion. It focuses on criterion satisfaction, missing evidence, and contradictions, supporting evaluation of models that apply domain rules and produce verification summaries. Typical uses include domain-specific model evaluation, rule-based reasoning research, and structured-output testing.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| case_record | object | Original case information, examination findings, candidate diagnoses, and diagnostic criteria supplied for verification. |
| overall_summary | string | Derived summary of verification across all candidate diagnoses, including important limitations or unresolved evidence. |
| verification_results | array | Derived criterion-by-criterion conclusions for each candidate diagnosis. |
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 |
Frequently Asked Questions
- What does the benchmark contain?
- Records are organized as JSON and include case information, examination findings, candidate diagnoses, and diagnostic criteria, along with criterion-level verification results and an overall summary to help trace conclusions and unresolved evidence.
- Which capabilities can it evaluate?
- It can be used to assess whether a model checks case evidence against explicit diagnostic criteria, identifies unmet or missing conditions, detects conflicting evidence, and summarizes its conclusions in a structured format.
- How can I access and use the data?
- Check the current product page for available files, download instructions, and usage guidance. The data is structured as JSON; review the field definitions and parse the case records and verification results as needed for your project.
- Is this suitable for clinical diagnosis, and what are the licensing terms?
- This benchmark is intended for domain evaluation and research, not as a substitute for diagnosis or clinical decisions by qualified professionals. Refer to the product page or provider's terms for licensing, redistribution, and commercial-use conditions.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Neuro-Ophthalmology Diagnostic Criteria Constraint Verification Benchmark},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/6ff111e5f945272c266c320f68ed95e8?dataset_scene_cate_type=3},
urldate={2026-10-26},
keywords={neuro-ophthalmology dataset,diagnostic criteria verification,medical AI benchmark,case constraint verification,medical rule reasoning,diagnostic criteria checking,clinical decision evaluation,JSON medical data},
version={1.0}
}Using this in research? Please cite us.
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