Artificial Intelligence-Based Triage and Diagnostic Support for Gastric Biopsies: A Multi-Centre Prospective Evaluation Across NHS Wales

Background: Histopathology workforce shortages and operational backlogs severely delay gastric cancer reporting. This multi-centre prospective evaluation assessed the diagnostic accuracy, clinical safety-net utility, and workflow triaging efficiency of an artificial intelligence (AI) platform for gastric biopsies across NHS Wales. Methods: A prospective cohort of 848 consecutive gastric biopsy cases across three NHS Health Boards was processed using a commercial AI platform. Diagnostic performance was evaluated against standard-of-care clinical reference diagnoses. Post-analytic cross-tabulation simulated dynamic AI-driven worklist prioritisation to quantify triage safety, false-positive alerts, and turnaround time impact across urgent and routine pathways. Results: For gastric malignancies (n = 40), the AI platform achieved a sensitivity of 100.0% (95% CI: 91.2–100.0%; AUC 0.986) and a specificity of 97.2% (95% CI: 95.8–98.1%). Sensitivities for low-grade dysplasia/adenoma (n = 12) and gastric intestinal metaplasia (n = 98) were 100.0% and 94.9%, respectively. The AI review functioned as a parallel safety net, capturing eight primary lesions missed during routine human evaluation, including one occult adenocarcinoma. In workflow simulation, the platform correctly prioritised 100.0% of cancers, including 6 cancers hidden within routine backlogs, and 97.2% (106/109) of high-risk non-cancer precursors, with false-positive alerts in 4.6% (39/848) of all cases. Conclusions: AI-assisted gastric pathology review demonstrates exceptional diagnostic accuracy, acts as a reliable clinical support tool, and enables effective risk-stratified triage. Deploying AI triage in public healthcare infrastructure offers a scalable solution with the potential to reduce diagnostic backlogs and accelerate treatment for high-risk oncology patients.