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

Table 3 Representative primary studies of artificial intelligence-assisted endoscopic documentation and report generation

Ref.	System/input	Setting/sample	Clinical function	Design/comparator	Evaluation unit	Key findings	Validation/main limitation
Qu et al[18], 2020	ISRGS; endoscopic images/video	46987 images; prospective set: 5699 images	Site/lesion recognition and structured report generation	Multicenter development and prospective validation	Image/patient	Overall diagnostic accuracy: 88.41% multicenter; 89.65% prospective	Multicenter data, but structured/template-based output
Dong et al[65], 2022	EAIRS; real-time EGD video	210198 development images; 161 prospective patients	Automated photo-documentation	Internal, external and prospective testing; system vs endoscopists	Patient/procedure	Landmark capture accuracy 95.2%; completeness: 93.1% vs 88.8%; detected-lesion capture: 99% vs 98%	Multicenter development; mainly image documentation rather than full narrative report
Yokota et al[67], 2022	Voice Capture/speech input	356 patients; 101 propensity-matched pairs	Speech-assisted structured reporting	Retrospective propensity-matched comparison; voice recognition vs manual entry	Procedure	Median report preparation time: 311 s vs 383 s, P = 0.009	Single-center retrospective study
Zhang et al[19], 2023	AI-EARS; EGD video/image	252111 images for training, 62706 images, and 950 videos for testing	Automatic image capture, diagnosis and textual description	Multireader multicase randomized crossover; AI-EARS vs conventional reporting	Report/procedure	Text completeness: 79.03% vs 51.86%; accuracy: 64.47% vs 42.81%; reporting time: 46.47 s vs 80.13 s	Relatively small clinical crossover sample
Naito et al[20], 2025	AR-C1; video/procedural events	40 endoscopic mucosal resection procedures	Procedure-event capture and report support	Retrospective clinical comparison	Procedure/report	Report generation time: 186.6 s vs 258.4 s	Single-center and single examiner
You et al[66], 2026	AI gastric-lesion reporting system	276 video clips; prospective clinical analysis: 100 patients	Structured lesion documentation	Prospective AI-assisted vs routine reporting	Lesion/report	Overall lesion-report completeness: 86.5% vs 75.11%, P = 0.001	Prospective but single-center
Kawagoe et al[68], 2026	EVAS voice recognition	21 cases; 5 experienced endoscopists	Voice-assisted colonoscopy reporting	Pilot comparison; EVAS vs manual typing	Report	Mean reporting time: 29 s vs 56 s; 46.3% reduction	Very small pilot; recognition accuracy/system integration require further evaluation
Jiang et al[21], 2026	Report-Angel; endoscopic images/videos + text	20617 training image-text pairs; prospective internal n = 300 and external n = 300 cohorts	MLLM-generated draft EGD reports	Prospective internal and external clinical validation; expert review of generated drafts	Case/report + lesion	Clinically acceptable draft reports: 79.3% internal, 83.3% external; case completeness 88.51%, accuracy 78.93%	Strongest current prospective MLLM evidence; generated drafts still require clinician review
Massimi et al[71], 2026	Gemini 2.5 Pro; complete EGD video	5 full-length EGD videos	MLLM video analysis and report generation	Retrospective paired pilot; clean video vs same video with CAD overlay	Report/event	Adequate completeness: 56% vs 48%; landmark accuracy: 0.55 vs 0.33	Extremely small pilot; authors concluded performance was inadequate for clinical EGD reporting

Outcomes are reported as task-specific findings from the original studies and are not intended for direct comparison across different tasks, study designs, or evaluation metrics. AI: Artificial intelligence; EGD: Esophagogastroduodenoscopy; LLM: Large language model; MLLM: Multimodal large language model; CAD: Computer‐aided detection/diagnosis.