Multimodal artificial intelligence and large language models in gastrointestinal endoscopy: From lesion detection to workflow-integrated report generation

Table 4 Proposed clinician-in-the-loop evaluation framework for artificial intelligence-generated endoscopy reports

Dimension	Clinician assessment	Scale	Suggested outcome
Factual grounding	Verify findings against source evidence	Item-level correct/incorrect	Accuracy; hallucination rate
Completeness	Check required report elements	Present/absent	Completeness; omission rate
Clinical safety	Grade consequences of errors	Minor/moderate/major	Major-error rate
Overall acceptability	Determine need for substantive correction	Yes/no	Clinically acceptable report rate
Workflow utility	Assess usability and editing burden	Likert scale + time	Utility score; correction time

For clinician-rated endpoints, evaluation by multiple blinded reviewers and reporting of inter-rater agreement (e.g., Fleiss’ κ or intraclass correlation coefficient) are recommended. This framework represents an author-proposed synthesis of current evaluation practices and is not a validated clinical guideline.