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

{}

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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Cite this Work

@dataset{Mobiusi2026,
  title={Neuro-Ophthalmology Diagnostic Criteria Constraint Verification Benchmark},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/6ff111e5f945272c266c320f68ed95e8},
  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}
}

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