Watson For Oncology (WFO): Developed With AI To Assist Oncologists In Treatment Decision-Making By Providing Evidence-Based Treatment Recommendations

Watson For Oncology (WFO): Developed With AI To Assist Oncologists In Treatment Decision-Making By Providing Evidence-Based Treatment Recommendations

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Qing Xu MD Of Shanghai Tenth Hospital Discusses Watson For Oncology (WFO): Developed With AI To Assist Oncologists In Treatment Decision-Making By Providing Evidence-Based Treatment Recommendations. BACKROUND: IBM Watson for Oncology (WFO) is developed with artificial intelligence to assist the oncologists in treatment decision-making by providing evidence-based treatment recommendations with priority and personality. We have been exploring the value of applying WFO in teaching and remote consulting among members in the hospital union to improve medical quality and conformity. METHODS: Four modes were followed to apply WFO to teaching and remote consulting. Firstly, a teaching hospital conducted instructional teaching for a primary hospital through WFO, or a primary hospital consulted with a teaching hospital on cancer treatment combined with WFO. Secondly, a teaching hospital conducted conference-based teaching and remote consulting for two or three primary hospitals through demonstrating WFO. Thirdly, we cooperated with other teaching hospitals to carry out interactive discussions on cases through WFO with several primary hospitals. Fourthly, we cooperated with other teaching hospitals to conduct WFO demonstration and discussion for several primary hospitals. Remote communication was performed by using third-party video conference software. A survey was conducted to collect feedback from 56 primary hospitals in our hospital union. RESULTS: More than 80% of primary hospitals were willing to learn the standardized treatment and recent treatment progress of tumors by participating in WFO remote consulting in our hospital. The value of four modes to promote the standardized cancer treatment and improve the medical quality and conformity in primary hospitals were all recognized. WFO also contributed to academic exchanges and learning among high-level teaching hospitals, and facilitated the use of artificial intelligence in oncology therapy more rational and appropriate. CONCLUSIONS: Non-standardization in cancer care remains to be a big problem in primary hospitals in China. Although limitation of WFO exists in the complex cases of certain tumor types, the rational use of WFO in the teaching and remote consulting could help and promote the standardized cancer treatment in China.
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