Abstract
The electrocardiogram is one of the most used medical tests worldwide. Despite its prevalent use in the healthcare sector, there exists a limited understanding in how medical practitioners interpret it. This is mainly due to the scarcity of international guidelines that unify its interpretation across different health institutions. This leads to a lack of training and unpreparedness by medical students who are about to join the medical workforce. In this paper, we propose a blueprint for a proactive artificial intelligence and augmented reality-based eye tracking system to train cardiology professionals for a better electrocardiogram interpretation. The proposed blueprint is inspired from extensive interviews with cardiology medical practitioners as well as students who interpret electrocardiograms as part of their daily practice. The interviews contributed to identifying the major pain-points within the process of electrocardiogram interpretation. The interviews were also critical in conceptualizing the persuasive components of the training system for a guided correct electrocardiogram interpretation. Throughout the presented blueprint, we detail the three components that constitute the system. These are the augmented reality-based interactive training interface, the artificial intelligence-based processing sub-system, and finally the adaptive electrocardiogram dataset.
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Sqalli, M.T., Al-Thani, D., Elshazly, M.B., Al-Hijji, M. (2022). A Blueprint for an AI & AR-Based Eye Tracking System to Train Cardiology Professionals Better Interpret Electrocardiograms. In: Baghaei, N., Vassileva, J., Ali, R., Oyibo, K. (eds) Persuasive Technology. PERSUASIVE 2022. Lecture Notes in Computer Science, vol 13213. Springer, Cham. https://doi.org/10.1007/978-3-030-98438-0_17
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