Efficacy of artificial intelligence-assisted upper gastrointestinal endoscopy for neoplasm detection: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND AND AIMS: EGD remains the primary diagnostic modality for evaluating lesions of the upper gastrointestinal (UGI) tract. This meta-analysis assessed the diagnostic performance of artificial intelligence-EGD (AI-EGD) compared with conventional EGD (Co-EGD) for the detection of UGI neoplasms. METHODS: A systematic search of major bibliographic databases identified randomized controlled trials (RCTs) comparing AI-EGD and Co-EGD in adults undergoing EGD. The primary outcome was neoplasm detection rate (NDR) per patient. Secondary outcomes included NDR per lesion and stratification by lesion size (<10 and ≥10 mm), as well as NDR by histologic classification: low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and carcinoma. Risk ratios (RRs) with 95% CIs were pooled using a random-effects model. RESULTS: Eleven RCTs involving 57,512 participants were included. AI-EGD demonstrated higher detection rates per patient (RR, 1.57; 95% CI, 1.23-2.01) and per lesion (RR, 1.55; 95% CI, 1.33-2.18). The benefit was more evident for lesions ≤10 mm (RR, 2.24; 95% CI, 1.72-2.91), whereas detection rates for lesions >10 mm were comparable. In addition, AI-EGD achieved higher detection rates across histologic subtypes, including LGIN (RR, 1.73; 95% CI, 1.30-2.32), HGIN (RR, 1.61; 95% CI, 1.39-1.87), and carcinoma (RR, 1.54; 95% CI, 1.32-1.79). CONCLUSIONS: AI-EGD demonstrated superior performance compared with Co-EGD in detecting UGI neoplasms, particularly for lesions ≥10 mm. The greater detection rate of LGIN highlights the potential clinical value of AI support. Further trials are warranted to assess the impact of AI across varying levels of endoscopist experience.