Effectiveness of Artificial Intelligence-Assisted Examination for Cancer Detection in Medical Imaging: A Systematic Review and Meta-Analysis.

[OBJECTIVE] To evaluate the effectiveness of artificial intelligence (AI)-assisted examination for cancer detection in medical imaging.

[METHODS] We searched seven databases from January 1, 2017, until June 30, 2024, to identify randomized controlled trials (RCTs). The primary outcomes were detection rates and patient-centered outcomes. Pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated.

[RESULTS] We included 49 RCTs covering seven cancer types, with 79.6% (n = 39) being colorectal cancer. AI-assisted examination showed varying effects on detection rates across different cancer types. Specifically, regarding colorectal cancer, AI increased detection rates for both adenoma (pooled RR = 1.22, 95% CI: 1.17-1.28, 36 RCTs) and polyp (pooled RR = 1.20, 95% CI: 1.14-1.26, 28 RCTs). For esophageal cancer, positive effects were also observed on the detection rates of high-risk esophageal lesions (RR = 2.01, 95% CI: 1.06-3.80, 1 RCT) as well as superficial esophageal squamous cell carcinoma and precancerous lesions (RR = 1.38, 95% CI: 1.03-1.86, 1 RCT). Moreover, statistically significant improvement in detection rates were observed in prostate cancer (pooled RR = 1.40, 95% CI: 1.10-1.77, 1 RCT with 3 arms), actionable lung nodules (RR = 2.38, 95% CI: 1.25-4.55, 1 RCT) for lung cancer, and breast cancer (RR = 1.20, 95% CI: 1.00-1.45, 1 RCT). However, no significant effect was observed on the detection rates of gastric or liver cancer.

[CONCLUSIONS] AI-assisted examinations may improve certain detection rates but not all among seven cancer types. There is a notable lack of patient-centered outcomes, crucial for evaluating the ultimate benefits to patients. Future research should give priority to assessing the impact of AI on patient-centered outcomes beyond diagnostic accuracy.