Tag Archives: artificial intelligence

Introduction Despite advances in managing atrial fibrillation (AF), it remains a major contributor to cardiovascular morbidity and mortality,1 placing a significant burden on both public health costs and the healthcare system.1 Cardiology is at the forefront of the artificial intelligence (AI) revolution within medicine, integrating AI with traditional diagnostic methods for timely interventions. For example, an AI-driven tool is the 10-second AI-enabled electrocardiogram (ECG), which could detect or even predict AF in patients who may have otherwise gone undiagnosed at the point of care.2-4 By identifying AF earlier, this technology has the

Introduction Acute coronary occlusions involving the diagonal or intermediate branches present diagnostic challenges, since classical patterns of ST-elevation in contiguous leads on electrocardiogram (ECG) are often not apparent. This leads to delays in catheter laboratory activation and delivery of reperfusion therapy, and, ultimately, worse clinical outcomes. Case report A 44-year-old man with a background of hypertension and paraplegia presented with acute chest pain radiating to the left arm, which woke him from sleep. He called for emergency medical assistance, and an ECG, performed by paramedics, showed ST-segment elevation in leads aVL

Introduction Medicine has benefited from increasingly advanced diagnostic and therapeutic options, which enable more tailored patient-specific strategies, with improvements in both efficacy and safety. Artificial intelligence (AI) was first researched in 1955 when John McCarthy proposed a project that attempted to “make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”1 In the 1970s, a new probabilistic model was developed that could simulate the process of expert decision-making by assigning weight to every clinical finding to indicate its possibility of occur

Introduction Artificial intelligence (AI) is poised to revolutionise cardiology over the next decade, offering unprecedented potential and exciting advancements. The immense burden of cardiovascular disease in the population provides cardiologists with a huge swathe of rich medical data, yet at the moment this is still underutilised. Machine learning and deep learning are subsets of AI that learn from data, rather than being specifically programmed, to identify new patterns and produce decision-making models.1 From improving diagnostic accuracy to enhancing treatment strategies, machine learning has the power to reshape patient care and outco

What is the future of cardiovascular health? NHS Medical Director Professor Sir Stephen Powis opened the conference by outlining the growing need to provide high quality cardiovascular care. With a quarter of deaths in England attributable to cardiovascular disease and a wider cost to the economy of £15.8 billion per year,1 there is an urgent need for innovative care pathways and new technologies. He showcased virtual wards as one example of innovation, with over 100,000 patients having been managed remotely in 2022.2 In Liverpool, a Telehealth team has successfully utilised a medical monitoring app to manage patients at home, leading to a 1

The challenges All these data, however, pose a serious challenge for physicians: the challenge of limitless choice. According to a white paper by Stanford Medicine,4 “the sheer volume of health care data is growing at an astronomical rate: 153 exabytes (one exabyte = one billion gigabytes) were produced in 2013 and an estimated 2,314 exabytes will be produced in 2020, translating to an overall rate of increase at least 48 percent annually.” With so much data on the daily decisions of millions of patients about their physical activity, dietary intake, medication adherence, and self-monitoring (e.g. blood pressure, weight), to name but a fe