Technical Writing and Biostatistics in ClinMAP-VOI v0: A Clinical AI-Safety Benchmark

Clinical AI benchmarks must satisfy two competing demands: technical precision in task design and statistical rigor in capturing human judgment. In ClinMAP-VOI v0, I have constructed a clinical AI-safety benchmark to evaluate vision-of-interpretation (VOI) robustness in medical AI systems through scenario-based clinical reasoning tasks. This paper details the documentation standards adapted from ICH-GCP (good clinical practice), the rubric design methodology, and the inter-rater reliability framework that underpins the benchmark. Technical writing standards ensure reproducibility and clinical validity; statistical validation through Fleiss' kappa quantifies multi-rater agreement on discrete clinical judgments. The benchmark demonstrates that systematic, replicable evaluation of clinical AI behavior in real-world diagnostic and decision-making conditions is feasible. Lessons from this work inform the design of future clinical AI evaluation frameworks.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-12
DOI
https://doi.org/10.5281/zenodo.21327398
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Technical Writing and Biostatistics in ClinMAP-VOI v0: A Clinical AI-Safety Benchmark

Tarek Ahmed Ibrahim Etman
Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
article

Technical Writing and Biostatistics in ClinMAP-VOI v0: A Clinical AI-Safety Benchmark

Tarek Ahmed Ibrahim Etman
article en

Abstract

Clinical AI benchmarks must satisfy two competing demands: technical precision in task design and statistical rigor in capturing human judgment. In ClinMAP-VOI v0, I have constructed a clinical AI-safety benchmark to evaluate vision-of-interpretation (VOI) robustness in medical AI systems through scenario-based clinical reasoning tasks. This paper details the documentation standards adapted from ICH-GCP (good clinical practice), the rubric design methodology, and the inter-rater reliability framework that underpins the benchmark. Technical writing standards ensure reproducibility and clinical validity; statistical validation through Fleiss' kappa quantifies multi-rater agreement on discrete clinical judgments. The benchmark demonstrates that systematic, replicable evaluation of clinical AI behavior in real-world diagnostic and decision-making conditions is feasible. Lessons from this work inform the design of future clinical AI evaluation frameworks.

Zenodo (CERN European Organization for Nuclear Research)
Dr. R. Ahmed Dental College and Hospital (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 11%
Artificial Intelligence in Healthcare and Education
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