Publications by authors named "Babita Saini"
Article Synopsis
- - A systematic review and meta-analysis examined the effectiveness of AI algorithms in screening for aortic stenosis (AS), finding that they can accurately diagnose the condition before severe symptoms develop.
- - The analysis included data from diverse sources (like ECGs and wearable sensors) and assessed various diagnostic metrics, concluding with a sensitivity of 83% and specificity of 81% for the AI algorithms.
- - Results indicated high diagnostic accuracy (AUC of 0.909), while various factors (geographic region, AS type, data sources, AI methods) contributed to variations in performance, with a noted potential for publication bias affecting the findings.
Objectives This study aimed to analyze the impact of the United States Medical Licensing Examination (USMLE) Step 1 transition to a pass/fail scoring system in 2022 on the performance of first-time test takers in three distinct groups: Doctor of Osteopathy (DO) and Doctor of Medicine (MD) examinees from US/Canadian schools and examinees from non-US/Canadian schools. The analysis spans a decade-long period from 2012 to 2022, offering insights into the implications of this pivotal change in medical education. Methods We analyzed the performance of first-time USMLE Step 1 examinees from US/Canadian MD and DO programs and non-US/Canadian schools from 2012 to 2022, including the transition year to a pass/fail scoring system.
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