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Effectiveness of Artificial Intelligence-Assisted Examination for Cancer Detection in Medical Imaging: A Systematic
Jinlu Song1, Yinyan Gao1, Wenqi Liu1
1Xiangya School of Public Health, Central South University, Changsha, Hunan, China.
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.
Insights
Artificial intelligence (AI) in medical imaging improves cancer detection rates for colorectal, esophageal, prostate, lung, and breast cancers. However, AI shows no significant effect on gastric or liver cancer detection and lacks patient-centered outcome data.
Area of Science:
- Medical Imaging
- Oncology
- Artificial Intelligence
Background:
- Artificial intelligence (AI) is increasingly integrated into medical diagnostics.
- Evaluating AI's effectiveness in cancer detection across various imaging modalities is crucial.
Purpose of the Study:
- To assess the impact of AI-assisted examinations on cancer detection rates.
- To analyze patient-centered outcomes associated with AI in cancer diagnosis.
Main Methods:
- Systematic review of randomized controlled trials (RCTs) published between January 1, 2017, and June 30, 2024.
- Searched seven databases for relevant studies.
- Analyzed primary outcomes including detection rates and patient-centered outcomes, using pooled relative risks (RRs) and 95% confidence intervals (CIs).
Main Results:
- Included 49 RCTs across seven cancer types; 79.6% focused on colorectal cancer.
- AI significantly improved detection rates for colorectal (adenoma RR=1.22, polyp RR=1.20), esophageal (high-risk lesions RR=2.01), prostate (RR=1.40), lung nodules (RR=2.38), and breast cancer (RR=1.20).
- No significant improvements were observed for gastric or liver cancer detection rates.
Conclusions:
- AI-assisted examinations demonstrate potential for enhancing cancer detection rates in specific types.
- A significant gap exists in research on patient-centered outcomes, hindering a full evaluation of AI's clinical benefit.
- Future research should prioritize patient-centered outcomes to comprehensively assess AI's role in cancer care.
