Comparison of case-based learning using Watson for oncology and traditional method in teaching undergraduate medical students

Abstract

BackgroundWatson for Oncology (WFO) is a decision-making system generated by artificial intelligence (AI) and has been widely used in treatment recommendations of cancer patients. However, the application of WFO in clinical teaching among medical students has not been reported. ObjectiveTo establish a novel teaching and learning method with WFO in undergraduate medical students and evaluate its efficiency and students' satisfaction compared with traditional case-based learning model. Methods72 undergraduates majoring in clinical medicine in Wuhan University were enrolled and were randomly divided into the WFO-based group and the control group. 36 students in the WFO-based group learned clinical oncology cases via WFO platform while 36 students in the control group using traditional teaching methods. After the course, final examination and questionnaire survey of teaching assessment were conducted on the two groups of students. ResultsAccording to the questionnaire survey of teaching assessment, WFO-based group showed significant higher score in the aspect of cultivating ability of independent learning (17.67 ± 1.39 vs. 15.17 ± 2.02, P = 0.018), increasing knowledge mastery (17.75 ± 1.10 vs. 16.25 ± 1.18, P = 0.001), enhancing learning interest (18.41 ± 1.42 vs. 17.00 ± 1.37, P = 0.002), increasing course participation (18.33 ± 1.67 vs. 15.75 ± 1.67, P = 0.001) and the overall course satisfaction (89.25 ± 5.92 vs. 80.75 ± 3.42, P = 0.001) than those of the control group students. ConclusionOur practice has established a novel clinical case-based teaching pattern with WFO, providing undergraduate students with convenient and scientific training and guidance. It empowers students with improved learning experiences and equips them with essential tools for clinical practices.