Artificial wisdom in gastrointestinal diagnostics: A new frontier in translational reasoning

Figure 1 Comparison of narrow artificial intelligence systems and artificial wisdom frameworks in gastrointestinal endoscopy. This table outlines key differences across ten dimensions relevant to clinical application. Narrow artificial intelligence (AI) systems are characterized by task-specific pattern recognition, single-modality data input, and opaque reasoning, often lacking integration with clinical workflows or patient context. In contrast, Artificial Wisdom frameworks emphasize multimodal data synthesis, causal reasoning aligned with clinical guidelines, calibrated uncertainty, and patient-centered decision support. They also prioritize transparency, equity, regulatory compliance, and outcome utility, reflecting a more holistic and clinically integrated approach to AI in gastrointestinal endoscopy. AI: Artificial intelligence; GI: Gastrointestinal.

Figure 2 Artificial wisdom-enabled decision pathway for colorectal polyp management. A layered framework integrating real-time endoscopic perception, patient-specific historical data, probabilistic reasoning, and clinician-facing recommendations to guide polyp management. The pathway incorporates morphology classification (Paris), surface pattern analysis (NICE, JNET), and vascular architecture assessment, alongside contextual factors such as prior polyp burden and surveillance history. Outputs include resection technique guidance, referral and tattooing recommendations, and surveillance interval suggestions, culminating in a structured endoscopy report. SSL: Sessile serrated lesion.