Copyright: ©Author(s) 2026.
World J Gastroenterol. Jun 7, 2026; 32(21): 117299
Published online Jun 7, 2026. doi: 10.3748/wjg.v32.i21.117299
Published online Jun 7, 2026. doi: 10.3748/wjg.v32.i21.117299
Table 3 The impact of artificial intelligence assistance on the detection of high-risk gastric lesions by endoscopists of different levels of experience in propensity score matching groups
| Endoscopist seniority | OR (95%CI) | P value |
| Senior (> 10 years) | ||
| LGIN | 1.96 (1.12-3.41) | 0.018 |
| HGIN | 3.67 (1.02-13.18) | 0.046 |
| EGC | 3.35 (1.34-8.34) | 0.010 |
| HrGLs | 2.23 (1.37-3.61) | 0.001 |
| Medium (5-10 years) | ||
| LGIN | 1.17 (0.39-3.48) | 0.782 |
| HGIN | 3.00 (0.61-14.89) | 0.178 |
| EGC | 2.20 (0.77-6.35) | 0.143 |
| HrGLs | 1.55 (0.72-3.31) | 0.260 |
| Junior (< 5 years) | ||
| LGIN | 3.01 (0.31-28.96) | 0.341 |
| HGIN | 2.00 (0.18-22.1) | 0.992 |
| EGC | 1.33 (0.30-5.97) | 0.992 |
| HrGLs | 1.50 (0.25-9.01) | 0.656 |
- Citation: Ying JX, Yan SY, Fu XY, Zhou YJ, Zhou JJ, Yang Y, Zhou XB, Wang ZZ, Li SW, Fang LN, Mao XL. Artificial intelligence-assisted endoscopists improve the detection rate of high-risk gastric lesions: A propensity score-matched retrospective study. World J Gastroenterol 2026; 32(21): 117299
- URL: https://www.wjgnet.com/1007-9327/full/v32/i21/117299.htm
- DOI: https://dx.doi.org/10.3748/wjg.v32.i21.117299