Health does not necessarily equate to disease free. It’s more about the idea of well-being. Some individuals may have chronic conditions yet they can still attain a good balance of life through appropriate diets, exercises, and carrying on with their daily routines.
At the 2019 Nuclear Cardiology & Cardiac CT (ICNC) conference, which ends today (14 May) in Lisbon, Portugal, Dr. Marco Mazzanti from the Royal Brompton Hospital of London had presented ARTICA, a machine learning driven decision-making system which outperform human cardiologists, in preventing unnecessary tests in patients with stable chest pain.
Stable chest pain is a condition which resulted in recurrent visits to general practitioners and emergency departments. Of all the 982 recruited patients, ARTICA advised 67% of them to discontinue further tests while human cardiologists only recommended 4.6% of the patients to do so. A computed tomography angiography (CTA) performed thereafter discovered confirmed ARTICA’s decision. Those who had been denied for further testing do not have the risk of suffering a significant coronary artery disease.
From treatment to prevention
Dr. Mazzanti is confident that “AI has the potential to save costs and staff time by identifying patients with chest pain who do not have significant coronary artery disease and therefore do not need expensive cardiac imaging.” Likewise, in a separate study, Dr. Luis Eduardo Juarez-Orozco of the Turku PET Centre in Finland repetitively analyzed 85 variables in 950 patients with known six-year outcomes and feeds them into a machine learning algorithm, which managed to identify patterns that correlates these factors with death and heart attack with over 90% accuracy.
“Doctors already collect a lot of information about patients… We found that machine learning can integrate these data to accurately
Gradually, medicine or health will no
A more holistic assessment
The propagation of technology in medicine and healthcare also signifies that our body condition will no longer be monitored within a particular setting. The point of care will widen, be it during our sleep, daily activities, or meal time, we will not be able to escape the assessments of a machine or digital device. In the near future, our visits to any healthcare professionals will not be dependent on us “feeling ill” but one of our monitoring devices had recommended us the visit due to a detected abnormality.
The trend may drive us to be dependent on machines for health monitoring. In turn, we may become more careless about our wellbeing. Like the
A science writer with data background and an interest in the current affair, culture,