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Leveraging Datathons to Teach AI in Undergraduate Medical Education: Case Study
JMIR Med Educ 2025;11:e63602
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Our analyses, based on simulated data, suggest that a total sample size of 300 provides enough statistical power (up to 90%) to detect anticipated effect sizes on primary outcomes of cognitive capacity and cognitive bias based on values from our group and those reported in the literature (CR: d=0.50 and MCT: g=0.27) [9,20,27,31]. The attrition rate for our digital groups has been approximately 20%; we will nonetheless conservatively adjust for an attrition rate of 30%.
JMIR Res Protoc 2025;14:e63269
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Evaluating User Experience and Satisfaction in a Concussion Rehabilitation App: Usability Study
Screenshots of the Rhea mobile app from left to right display (A) the home page of a participant’s plan, (B) a symptom checklist during onboarding that assists with exercise prescription, (C) an exercise preview screen providing description and cues for correct form, and (D) an exercise session screen with a timer and details of the next exercise for the user.
JMIR Form Res 2025;9:e67275
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(C) and (D) Performance of training on BIDMC data and testing on MGH data. AUPRC: area under the precision-recall curve; AUROC: area under the receiver operating characteristic curve; BIDMC: Beth Israel Deaconess Medical Center; MGH: Mass General Hospital; PR: precision-recall; ROC: receiver operating characteristic.
Logistic regression coefficients from using the model trained with notes, ICDa codes, and medications. Unexpected results are discussed in the Error Analysis section.
JMIR Med Inform 2025;13:e64113
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