A meta-analysis of Watson for Oncology in clinical application

Using the method of meta-analysis to systematically evaluate the consistency of treatment schemes between Watson for Oncology (WFO) and Multidisciplinary Team (MDT), and to provide references for the practical application of artificial intelligence clinical decision-support system in cancer treatment. We systematically searched articles about the clinical applications ofWatson for Oncology in the databases and conducted meta-analysis using RevMan 5.3 software. A total of 9 studies were identified, including 2463 patients. When the MDT is consistent with WFO at the 'Recommended' or the 'For consideration' level, the overall concordance rate is 81.52%. Among them, breast cancer was the highest and gastric cancer was the lowest. The concordance rate in stage I-Ill cancer is higher than that in stage IV, but the result of lung cancer is opposite (P < 0.05).Similar results were obtained when MDT was only consistent with WFO at the recommended level. Moreover, the consistency of estrogen and progesterone receptor negative breast cancer patients, colorectal cancer patients under 70 years old or ECOG 0, and small cell lung cancer patients is higher than that of estrogen and progesterone positive breast cancer patients, colorectal cancer patients over 70 years old or ECOG 1-2, and non-small cell lung cancer patients, with statistical significance (P < 0.05). Treatment recommendations made by WFO and MDT were highly concordant for cancer cases examined, but this system still needs further improvement. Owing to relatively small sample size of the included studies, more well-designed, and large sample size studies are still needed.

Automated summary

Using meta-analysis to evaluate the consistency between Watson for Oncology (WFO) and Multidisciplinary Team (MDT) treatment schemes, it was found that there is a high concordance rate in certain types of cancer and patient populations. However, further improvement is needed, and more studies with larger sample sizes are required to validate these findings.