AI and medicine
asymptomatic, without the need for extremely specialized equipment or a healthcare professional. The Atrial Fibrillation algorithm was trialled by Apple between 2017 and 2018 with the help of Stanford University’s School of Medicine in California , when a study was conducted to examine the feasibility and effectiveness of such a tool in reducing the number of hospitalizations due to undiagnosed Atrial Fibrillation. It involved just over 419,000 Apple Watch users and, over 8 months, 0.52% (2161) of wearers had a notification of irregularities involving the electrical stimuli in the atria. 450 of the participants who received the notification returned ECG findings and 34% of which were
found to have Atrial Fibrillation. Due to the fact that a very low proportion of Apple watch users received this notification, as well as discovery that 34% of receivers after further examination by a cardiologist had underlying A-Fib, Apple concluded that it was successful, and this technology was implemented in all of their subsequent Apple Watches. 5 Considering that over 6 million Americans live with A-Fib, and it is estimated another 700,000 may have undiagnosed A-Fib, this technology could prove life-saving as A-Fib can quintuple the risk of a stroke, which has become more of an issue in an increasingly unhealthy ageing population that. In the future, A-Fib will be even more commonplace and, with technology that almost everyone has access to, this could prove ground-breaking in preventing needless deaths and assist healthcare providers in testing and treating this disorder. The use of artificial intelligence and machine learning in medicine has two main disciplines: physical and virtual. The virtual branch focuses on information and how it is processed. This can be anything from AI diagnostics of X-rays or CT scans for the guidance of practising health professionals or simply how patient data is stored and managed electronically. Physical AI is the use of physical robots which find their use in remote surgery as well as surgical assistance. One major new development is the use of robots for drug delivery called ‘nanorobots’ . 6 They differ from traditional drug delivery, which is dependent on the random and unpredictable movement of the cardiovascular system to reach a target; the designed micro/nanorobots can move autonomously or are controlled by an operator, which makes it possible to deliver drugs to the hard-to-reach areas and to eliminate the effect or unpredictability of human systems to get better treatment results. The NHS often struggles to find enough beds for patients, a situation that has become more acute during and after the COVID-19 pandemic, and one of the solutions currently being explored is virtual reality (VR). This is used in a new program run by NHS England which uses VR smart goggles. They aim to increase the number of home visits to outpatients which, it is hoped, will then lead to fewer hospital stays. It comes with numerous tools such as the ability for the appointment to be recorded or streamed live so a second opinion can be obtained by a specialist, as well as a scribe function which automatically inputs notes directly into patient records. This innovation comes after a study conducted by NHS England found that community nurses spend over half their time at work doing mundane administration such as paperwork and the manual input of data. All of this has culminated in an £400,000 investment by the NHS and trial studies being conducted, the first of which is currently 5 See Perez, M et al. https://www.nejm.org/doi/full/10.1056/NEJMoa1901183. 6 See Hu, M. et al. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7407549/#:~:text=Differently%20from%20traditional%20dr ug%20delivery,hard-to-reach%20areas.
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