From hype to clinical translation: A tiered, readiness-based framework for artificial intelligence in gastrointestinal endoscopy

Table 2 Prospective checklist for advancing an endoscopic artificial intelligence system to the next tier

Question	Why it matters	Minimum evidence before advancing tier
Does the system improve a clinically meaningful endpoint rather than only image-level accuracy	Detection gains may not translate into patient benefit if they mainly increase low-value findings	At least one prospective study with workflow-relevant outcomes; tier 1 requires multicenter randomized or pooled randomized evidence
Is there a clear action pathway once the AI output is generated	Outputs without downstream decisions create ambiguity, delay, and liability risk	Explicit linkage between AI output and biopsy, resection, documentation, referral, or review pathway
Has performance been shown outside the development environment	Single-center or single-vendor success often overestimates real-world performance	External validation across centres, operators, and ideally more than one hardware ecosystem
Will deployment preserve safe human performance	Automation bias and deskilling can offset technical gains	Human-factors plan with onboarding, override logging, periodic AI-off benchmarking, and monitoring of behaviour-level metrics
Is governance defined before launch	Undefined responsibility undermines adoption and patient safety	Named accountability, update policy, discordant-case review, and AI-specific protocol or reporting aligned with CONSORT-AI or DECIDE-AI when applicable
Is post-deployment monitoring specified	Static pre-deployment evidence cannot detect drift, latency issues, or workflow changes	Named metrics, review frequency, trigger thresholds, rollback or recalibration plan, and change-control policy consistent with lifecycle guidance
Is the dataset and validation geography sufficiently representative	Geographic and demographic concentration limits generalizability and equity	Evidence of representation across populations, settings, and device environments relevant to intended deployment
Is the system economically and operationally sustainable	Clinical value may be offset by cost, follow-up burden, or proprietary infrastructure constraints	Context-specific implementation plan addressing costs, maintenance, reimbursement, and downstream utilization

AI: Artificial intelligence.