UCC Student Medical Journal Vol. 4 (2024)
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Item UCC Student Medical Journal 4th Edition(UCC Medical Research and Technology Society) Cronin, Padraig; Mehta, ShobhaItem Enter Modius: Neurovalens's innovative new solution to insomnia(UCC Medical Research and Technology Society) Iskander, AndrewItem A two-dimensional cellular automaton model of parasystole(UCC Medical Research and Technology Society, 2024) Kodancha, MeghaUnder normal cardiac conditions, the sinoatrial node is the pacemaking region which initiates depolarization in the heart; in parasystole, there also exists an ectopic pacemaker which may initiate depolarization waves. Parasystole is a form of arrhythmia caused by the influence of the secondary pacemaker on cardiac behaviour. Specifically, we consider cases of pure parasystole, where the two pacemakers are protected from each other. Previous theoretical models of pure parasystole consider the interaction of two pacemakers without incorporating physical space. The objective here is to create a simple, theoretical, two-dimensional model of pure parasystole where the distance between the pacemakers may be adjusted. A cellular automaton model was created using Python 3.8.3 and associated packages. The model was used to evaluate how changes in space influenced cell activation cycles and the number of intervening sinus beats (the number of times cells were activated by the sinus node versus being activated by the ectopic pacemaker). The model dynamics were further compared to experiments using optogenetic methods to stimulate a cardiac monolayer from two sites. This model provides insight into the physical dynamics of parasystole in its most basic form so that it may be built upon to eventually be used in a clinical context.Item UCC Student Medical Journal 4th Edition(UCC Medical Research and Technology Society, 2024) Cronin, Padraig; Mehta, ShobhaThe 4th Edition of the UCC Student Medical Journal.Item The use of Artificial Intelligence in clinical diagnostics: Challenges to consider for implementation(UCC Medical Research and Technology Society, 2024) Cronin, PadraigWhilst many technological advancements have revolutionised healthcare throughout the 21st century, one of the most significant is Artificial Intelligence (AI). AI is generally regarded as the capability to imitate intelligent human behaviour using machines, and is based on computer science, statistics, algorithms, machine learning, information retrieval, and data science1. AI has permeated into many domains of healthcare including Clinical Diagnostics. While AI chatbots (such as those used in Babylon and Ada) are being used by patients to identify symptoms and recommend further actions in community and primary care settings, more recent advances in the technology with larger datasets have provided users access to a more extensive array of clinical conditions2. However, as these tools are constantly being developed with an ever-increasing dataset of clinical cases, certain challenges threaten the implementation of an accurate and effective model. In this article, the issue of Data Bias, and Data Handling will be examined within the context of Clinical Diagnostics, and how these factors threaten the development of such AI Healthcare tools.
