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Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review
Robert Oehring1, Nikitha Ramasetti1, Sharlyn Ng1
1Department of Surgery, Charité - Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
Background:
For therapy planning in cancer patients multidisciplinary team meetings (MDM) are mandatory. Due to the high number of cases being discussed and significant workload of clinicians, Clinical Decision Support System (CDSS) may improve the clinical workflow.
Methods:
This review and meta-analysis aims to provide an overview of the systems utilized and evaluate the correlation between a CDSS and MDM.
Results:
A total of 31 studies were identified for final analysis. Analysis of different cancers shows a concordance rate (CR) of 72.7% for stage I-II and 73.4% for III-IV. For breast carcinoma, CR for stage I-II was 72.8% and for III-IV 84.1%, P≤ 0.00001. CR for colorectal carcinoma is 63% for stage I-II and 67% for III-IV, for gastric carcinoma 55% and 45%, and for lung carcinoma 85% and 83% respectively, all P>0.05. Analysis of SCLC and NSCLC yields a CR of 94,3% and 82,7%, P=0.004 and for adenocarcinoma and squamous cell carcinoma in lung cancer a CR of 90% and 86%, P=0.02.
Conclusion:
CDSS has already been implemented in clinical practice, and while the findings suggest that its use is feasible for some cancers, further research is needed to fully evaluate its effectiveness.
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