Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis

Abstract

BackgroundFor 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.MethodsThis review and meta-analysis aims to provide an overview of the systems utilized and evaluate the correlation between a CDSS and MDM.ResultsA 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.ConclusionCDSS 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.

High‐throughput molecular assays for inclusion in personalised oncology trials – State‐of‐the‐art and beyond

“…With the increasing complexity of patient clinical and genomic data, and the availability of a larger number of relevant knowledgebases, AI will increasingly be applied to assist in guiding clinical choices in an MTB setting [101]. Several limitations exist, including quality and update frequencies of underlying data, variability in different populations, availability of medical records and extent and type of genomic data and drug availability, which are all aspects in need of improvement.…”

“…So far, limited studies have been published on the relative performance of AI‐based CDSS tools. A relevant metric to evaluate is the concordance between the recommendations provided by the AI‐based method and the MTB, which varies from <60% to >90% as well as varies between tumour types [101]. Overall, although significant challenges still exist, CDSSs are key for an efficient implementation of precision oncology strategies, which greatly facilitate, but not substitute, the process of clinical interpretation provided by expert medical teams, such as that from an MTB.…”

The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review

“… 12 Several literature reviews have already been performed on AI and CDSS in specific domains such as in oncology, dentistry, or infectious disease. 23–25 However, none of these reviews focused on medication alerts generated by CDSS in hospitalized patients.…”