ArticleCureus2026
Human-Governed Validation of Artificial Intelligence-Generated Medical Assessment Artifacts: A Technical Report.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence (AI) models can create fluent medical assessment materials, including multiple-choice questions, distractors, clinical vignettes, answer explanations, and blueprint tags. However, linguistic plausibility does not establish measurement validity. Using AI-generated text as ready-made assessment material may introduce risks related to clinical accuracy, item-writing quality, blueprint alignment, distractor functioning, fairness, and score interpretation. This technical report presents a human-governed validation workflow that treats AI-generated medical assessment artifacts as candidates requiring staged review rather than immediate deployment. The workflow is organized into seven gates: artifact taxonomy, AI provenance and human accountability, clinical and content review, item-writing and language review, blueprint alignment, psychometric-readiness decision, and final human approval. The psychometric-readiness gate explicitly asks whether item analysis, distractor analysis, differential item functioning (DIF) review, or other empirical evaluation is required before an artifact is used in a scored assessment. By separating expert clinical review from psychometric evidence, the workflow preserves human accountability while acknowledging that expert approval alone cannot support all score-based interpretations. We recommend introducing AI-generated assessment materials into medical education pipelines as preliminary candidates subject to documented governance, human oversight, and empirical evaluation when used for scored or consequential assessment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.