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

Abstract

Artificial Wisdom represents a paradigm shift in AI-augmented clinical reasoning, integrating historical diagnostic expertise with real-time data, causal inference, uncertainty quantification, and patient-centered decision-making. In gastrointestinal (GI) diagnostics-particularly endoscopy-this approach transcends task-specific pattern recognition to support comprehensive, guideline-aligned decisions about lesion characterization, sampling, and therapy. This mini-review synthesizes the conceptual foundations of Artificial Wisdom and its translational potential in GI endoscopy, highlighting multimodal data integration, causal reasoning, and calibrated uncertainty as pillars of intelligent decision support. We examine current capabilities and limitations of AI systems, propose a three-tier architecture for translational reasoning, and present case-based applications across colorectal polyp management, inflammatory bowel disease surveillance, Barrett’s esophagus, and pancreaticobiliary diagnostics. Implementation challenges-including bias, workflow integration, and regulatory pathways-are discussed alongside research priorities for causally-informed artificial intelligence (AI), human-AI collaboration, and prospective validation. Endoscopy emerges as a proving ground for Artificial Wisdom, offering a pathway to more consistent, equitable, and personalized GI care.

Keywords: Artificial intelligence; Machine learning; Gastrointestinal endoscopy; Computer-aided detection; Polyp detection; Colonoscopy

Core Tip: Artificial Wisdom represents a paradigm shift in artificial intelligence-augmented gastrointestinal (GI) diagnostics, moving beyond narrow task-specific algorithms toward integrated, patient-centered reasoning. By combining multimodal data inputs, causal inference, calibrated uncertainty, and guideline-aligned decision support, Artificial Wisdom frameworks offer a more holistic and clinically meaningful approach to endoscopic decision-making. This mini-review outlines the conceptual foundations, architectural design, and translational applications of Artificial Wisdom in GI endoscopy, highlighting its potential to enhance diagnostic accuracy, equity, and workflow integration.