2020
DOI: 10.2196/14122
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Concordance Between Watson for Oncology and a Multidisciplinary Clinical Decision-Making Team for Gastric Cancer and the Prognostic Implications: Retrospective Study

Abstract: Background With the increasing number of cancer treatments, the emergence of multidisciplinary teams (MDTs) provides patients with personalized treatment options. In recent years, artificial intelligence (AI) has developed rapidly in the medical field. There has been a gradual tendency to replace traditional diagnosis and treatment with AI. IBM Watson for Oncology (WFO) has been proven to be useful for decision-making in breast cancer and lung cancer, but to date, research on gastric cancer is limi… Show more

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Cited by 29 publications
(19 citation statements)
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“…However, the statistical significance was only noted between the "recommended" group and "not recommended" group because of our small sample size, and limited follow-up time, which to some extent proved the effectiveness of WFO system to aid toward achieving a good prognosis. Additionally, similar to a recent study (12) that demonstrated that the overall survival of patients with gastric cancer in the concordant group was better than that in the non-concordant group, we further found that both, OS, and DFS were better in the concordant patients, although no statistical significance was reached for DFS. That also greatly attributed to the better prognosis of the 13 over treated patients in "physician's decision" group.…”
Section: Discussionsupporting
confidence: 89%
“…However, the statistical significance was only noted between the "recommended" group and "not recommended" group because of our small sample size, and limited follow-up time, which to some extent proved the effectiveness of WFO system to aid toward achieving a good prognosis. Additionally, similar to a recent study (12) that demonstrated that the overall survival of patients with gastric cancer in the concordant group was better than that in the non-concordant group, we further found that both, OS, and DFS were better in the concordant patients, although no statistical significance was reached for DFS. That also greatly attributed to the better prognosis of the 13 over treated patients in "physician's decision" group.…”
Section: Discussionsupporting
confidence: 89%
“…Currently, it is unclear how endoscopists would react to a diagnosis made using AI (examples from the literature include approval, a learning opportunity, or “presenting an indolent attitude”) [ 28 , 29 ]. Therefore, a prospective study based on the application of AI in clinical practice (more specifically, in diagnostic endoscopy) is essential [ 30 , 31 ]. However, providing robust answers using an AI algorithm irrespective of the endoscopists’ inspection would be helpful to increase the likelihood of identifying important findings in diagnostic endoscopy.…”
Section: Discussionmentioning
confidence: 99%
“…One could imagine situations in which different IMS have been used on, or are available for, a patient such that each suggests different intervention choices. For example, consider the rules-based intervention recommendations produced by IBM's Watson computing system 6,7 vs. those derived from the Connectivity Map. 8 These situations may motivate or warrant studies to see which of these IMS outperforms the other.…”
Section: Reviewmentioning
confidence: 99%
“…IBM Watson has been shown to compare favorably with physician-guided treatment decisions, although there is debate about the merits of the results of relevant studies. 6,7 However, there are alternatives to IBM Watson that leverage different algorithms or strategies for automating intervention decisions, so a good question is whether or not these alternatives could outperform IBM Watson in a head-to-head comparison. Thus, the need to vet different underlying strategies governing an RLS might be an issue in the future if an RLS was not created that evaluated the rules underlying each in an all-encompassing IMS.…”
Section: Real-world Data Learning Systemsmentioning
confidence: 99%