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

Table 1 Operational criteria used for tier assignment

Domain	Tier 1	Tier 2	Tier 3
Evidence maturity	Multicenter randomized evidence or pooled randomized data with clinically meaningful endpoints and well-described downstream consequences	Prospective multicenter, pooled randomized, or strong external-validation data, but patient-level or pathway consequences remain incomplete	Single-center, retrospective, pilot, or proof-of-concept predominance
Regulatory authorization/health-system pathway	Regulatory authorization for intended use plus either supportive or permissive practice-facing guidance (i.e., recommending in favour of or conditionally allowing the technology) or a structured evidence-generation pathway	Partial regulatory or health-system traction, pilot implementation, or pathway-facing evaluation, but convergence not yet present	No meaningful regulatory traction or practice-facing pathway
Real-world deployment	Use beyond expert development centers, including community or non-academic settings	Limited or early real-world implementation	No meaningful real-world deployment evidence
Workflow actionability	Clear clinical action path with feasible real-time integration into procedural workflow	Action path plausible but not standardized	Action path unclear, procedure-specific, or not yet clinically testable
Governance/monitoring	Explicit oversight with named metrics, update disclosure, override logging, and evidence-generation or post-market monitoring	Published protocol or institutional plan specifies some governance elements, but lifecycle monitoring remains incomplete	Governance largely undefined
Generalizability/resource fit	Evidence across heterogeneous populations, platforms, or settings, with plausible operational fit outside expert centers	Some external validation, but geographic or vendor concentration remains substantial	Narrow setting, platform, or population dependence

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.

Table 3 Priority translational agenda for the next 3 years to 5 years

Priority	Why it matters	Example deliverable
Patient-important outcomes for Tier 1 colonoscopy AI	ADR alone is no longer sufficient to justify broad adoption given that most incremental yield is from diminutive lesions	Multicenter registry or pragmatic trial accompanying (not following) rollout and measuring advanced neoplasia, interval cancer surrogates, low-value resection rate, and surveillance intensity
Pathway-defining trials for tier 2 tools	Technical accuracy does not establish net clinical value	Capsule AI trial measuring reading time, false-positive burden, downstream procedure rate, and cost; H. pylori study linking AI outputs to biopsy strategy and management
Global generalizability	Current literature is concentrated in a few regions and expert centres	Prospective validation across underrepresented geographies and lower-resource settings, including South Asia, Latin America, and sub-Saharan Africa
Human-factors safeguards as a standard requirement	Automation bias can erode clinician performance, as the deskilling signal in colonoscopy demonstrates	Mandatory AI-off benchmarking, override logging, and discordant-case review as part of every rollout protocol
Governance before guideline endorsement	Deployment without accountability is operationally fragile	Minimum package of update disclosure, drift monitoring, and escalation policy before any tier 2 system receives society-level endorsement
CADx pathway clarification	Two major meta-analyses show no net benefit of current CADx in routine practice; the resect-and-discard pathway requires society-level redefinition before AI-assisted optical diagnosis can be broadly implemented	Prospective pragmatic trial comparing AI-assisted resect-and-discard vs standard practice on histopathology concordance, surveillance interval assignment, and medicolegal framework

AI: Artificial intelligence; ADR: Adenoma detection rate; H. pylori: Helicobacter pylori; CADx: Computer-aided diagnosis.