New Validation Process for AI-Assisted Medical Evaluations
A seven-step, human-supervised workflow has been developed to enhance the reliability of artificial intelligence-generated materials in medical education.
A new seven-step technical validation workflow has been prepared to protect the clinical accuracy and editorial integrity of AI-generated content used in medical education.
Use of Artificial Intelligence in Medical Education and Risks
Medical education teams frequently encounter AI-assisted materials such as multiple-choice questions and answer explanations. Unverified AI outputs may contain clinical errors.
Although generative models produce fluent text, they also bring various risks such as weak distractors and curriculum mismatches.
Seven-Step Structured Validation Workflow
To minimize risks, a seven-step workflow has been defined that separates elements such as clinical review, item writing, and psychometric evidence.
This process was derived from practices in operations producing calibrated formative assessments for medical schools in Brazil.
Stages of the Workflow and Human Responsibility
The system consists of stages such as taxonomy, AI provenance, clinical review, language control, and psychometric preparation.
Each gate requires a documented human decision to ensure that materials pass through quality control processes.