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.
Authors
- Tarek Ahmed Ibrahim Etman
Institutions
- Dr. R. Ahmed Dental College and Hospital (IN)
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