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.