Large language models for detecting colorectal polyps in endoscopic images

Commercial, regulatory-approved computer-aided detection (CADe) tools are widely used in colonoscopy; however, open-source multimodal large language models may present a promising alternative. This study compared the polyp detection performance of two such models—GPT-4o and Gemini 1.5 Pro—with ENDO-AID CADe, a commercial artificial intelligence (AI) system, using colonoscopy videos. Per-lesion sensitivities were 75% for GPT-4o, 50% for Gemini and 87% for ENDO-AID. While the large language models were less effective at detecting polyps ≤5 mm, they performed comparably for larger lesions, suggesting their potential as alternative AI tools in endoscopic practice.