Can artificial intelligence save our lives?

The role of artificial intelligence (AI) in our life is advancing rapidly and is making strides in the early detection of diseases. The consumer market is composed of wearable health devices that enables continuous ambulatory monitoring of vital signs during daily life (at rest or physical activity), or in a clinical environment with the advantage of minimizing interference with normal human activities1. These devices can record a wide spectrum of vital signs, including: heart rate and rhythm, blood pressure, respiratory rate, blood oxygen saturation, blood glucose, skin perspiration, body temperature, in addition to motion evaluation. However, there is a lot of controversies whether these health devices are reliable and secure tools for early detection of arrhythmia in the general population2.

Atrial fibrillation (afib) is the most common arrhythmia currently affecting over 5 million individuals in the US and it’s expected to reach almost 15 million people by 2050. Afib is associated with an increased risk of stroke, heart failure, mortality, and represents a growing economic burden3. Afib represents a diagnostic challenge, it is often asymptomatic and is often diagnosed when a stroke occurs. Afib represents also a long term challenge and often involves hospitalization for cardioversion, cardiac ablation, trans-esophageal echo, anti-arrhythmic treatment, and permanent pacemaker placement. However, if afib is detected, the risk of stroke can be reduced by 75% with proper medical management and treatment3.

Physicians need fast and accurate technologies to detect cardiac events and assess the efficacy of treatment. A reliable, convenient and cost-effective tool for non-invasive afib detection is desirable. Several studies assessed the efficacy and feasibility of wearable technologies in detecting arrhythmias. The Cleveland Clinic conducted a clinical research where 50 healthy volunteers were enrolled. They tested 5 different wearable heart rate monitors including: (Apple Watch, Garmin Forerunner, TomTom Spark Cardio, and a chest monitor) across different types and intensities of exercises (treadmill, stationary bike and elliptical). The study found that the chest strap monitor was the most accurate in tracking the heart rate across different types and intensities of exercises4.

Apple and Stanford’s Apple Heart Study enrolled more than 419,297 Apple Watch and iPhone owners. Among these users, 2,161 (roughly 0.5%) received a notification of an irregular pulse. Of those who received the notifications, only about 450 participants scheduled a telemedicine consultation and returned a BioTelemetry ECG monitoring patch. When the Apple Watch notification and ECG patch were compared simultaneously, researchers found 71% positive predictive value, and about 84% of the cases were experiencing Afib at the time of the alert. Additionally, 34% of participants whose initial notification prompted an ECG patch delivery were later diagnosed with Afib. This finding shows that Apple watch detected afib in about one-third of the cases which is “good” for a screening tool considering the “intermittent nature of afib and that it may not occur for a whole week” says Dr. Christopher Granger, a professor of medicine at Duke University who participated on the steering committee for the Apple Heart study5.

These studies are observational studies and are not outcome-driven. They are not randomized and are not placebo-controlled. There are potentials for false negatives, where the Apple watch fails to detect the afib and false-positive where it detects arrhythmia that does not exist. Unfortunately, patients who are false negative don’t consult the physician about their symptoms of palpitations and shortness of breath since it provides false security. While patients with false-positive are sent unnecessarily to the clinic that could lead to further unnecessary tests and anxiety for the patient.

Is the Apple Watch ready to be used as a default screening tool to monitor the heart rate and rhythm in the general population and by physicians with patients with or at high risk for Afib is still unclear and warrant further studies. In conclusion, physicians should be cautious when using data from consumer devices to treat and diagnose patients.

The views, opinions and positions expressed within this blog are those of the author(s) alone and do not represent those of the American Heart Association. The accuracy, completeness and validity of any statements made within this article are not guaranteed. We accept no liability for any errors, omissions or representations. The copyright of this content belongs to the author and any liability with regards to infringement of intellectual property rights remains with them. The Early Career Voice blog is not intended to provide medical advice or treatment. Only your healthcare provider can provide that. The American Heart Association recommends that you consult your healthcare provider regarding your personal health matters. If you think you are having a heart attack, stroke or another emergency, please call 911 immediately.


  1. Cheung, Christopher C., Krahn, Andrew D., Andrade, Jason G. The Emerging Role of Wearable Technologies in Detection of Arrhythmia. Canadian Journal of Cardiology. 2018;34(8):1083-1087. doi:10.1016/j.cjca.2018.05.003
  2. Dias D, Paulo Silva Cunha J. Wearable Health Devices-Vital Sign Monitoring, Systems and Technologies. Sensors (Basel). 2018;18(8):2414. Published 2018 Jul 25. doi:10.3390/s18082414
  3. Chugh, S., Sumeet, Havmoeller, J., Rasmus, Narayanan, F., Kumar, et al. Worldwide Epidemiology of Atrial Fibrillation: A Global Burden of Disease 2010 Study. Circulation. 2014;129(8):837-847. doi:10.1161/CIRCULATIONAHA.113.005119
  4. Wrist-Worn Heart Rate Monitors Less Accurate Than Standard Chest Strap. Medical Design Technology. http://search.proquest.com/docview/1875621494/. Published March 9, 2017.
  5. Turakhia, Mintu P., Desai, Manisha, Hedlin, Haley, et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study. American Heart Journal. 2019;207:66-75. doi:10.1016/j.ahj.2018.09.002




Artificial Intelligence in Cardiology: Opportunities for Cardio-Oncology

History was made recently with the inaugural and first ever continuing medical education conference on artificial intelligence (#AI) in Cardiology. While most of the presentations were on artificial intelligence or cardiology or both, several sessions also made reference to other fields in which AI has been or is being used, such as Oncology. There was even one study presented on Cardio-Oncology. As study after study was presented, it became clear to me that perhaps several of these techniques and methodologies could potentially be useful to our patients in Cardio-Oncology.

Every single piece of technology started with one single prototype. Every single new piece of software started with one single algorithm. Every single patent started with one single idea. Every single idea started with the impact that disruptive technology could have for at least one single patient – one single case.

As I view various case reports in Cardio-Oncology, I think about how #AI could influence care delivery to potentially improve outcomes and the experience for each patient and their health professionals.

One example that was reiterated in multiple presentations was that of the ECG. Applying #AI to the ECG has been shown in the studies presented to determine the age, sex, and heart condition of the individual. Details were shown for a case of hypertrophic cardiomyopathy (yes, HCM, not just left ventricular hypertrophy) diagnosed via #AI analysis of an ECG that appeared relatively unremarkable to physicians’ eyes. After the septal surgery/procedure, although the ECG then looked remarkably abnormal to physicians’ eyes, the #AI algorithm could identify resolution of the hypertrophic cardiomyopathy.

Another example reiterated throughout the conference was identifying undiagnosed left ventricular systolic dysfunction, in a general community population and also in patients referred to a cardio-oncology practice at a large referral center.

Recently, #AI in Cardiology has been used most frequently for monitoring and detection of arrhythmias, such as atrial fibrillation. Everyone can purchase their own wearable to determine this. Physicians are also now prescribing these wearables for ease-of-use, given their pervasive presence and coupling with smartphones owned by much of the population or provided temporarily by the physician group. Such wearables are transitioning from standalone electrodes, to watches, skin patches, and clothing (e.g., shirts, shorts).

Many direct-to-consumer #AI applications in daily life actually are not wearable, such as Alexa and Siri. One study described the ability of #AI to help diagnose mood disorders and cardiac conditions and risk factors by simply “listening to” and analyzing voice patterns. The timing of a young man’s “voice breaking” can potentially predict his risk for heart disease!

A popular use for #AI in medicine overall is to assist with interpretation of various imaging, such as chest X-rays, MRIs, or CT scans. This applies in Cardiology as well. Further, in Cardiology, #AI is being used to help guide the procurement of echocardiograms. The algorithms provide visual instructions (such as curved arrows) to indicate directions in which the ultrasound probe should be moved to obtain the standard view, to which the algorithm is comparing the image being procured moment-by-moment. The idea is for #AI to help less experienced sonographers or echocardiographers learn and perform echocardiography even more expediently.

The theme of the conference was current advances and future applications of #AI in Cardiology. Accordingly, a historical perspective was given, describing some of the earliest attempts at #AI in various fields. A video of a possible precursor to current automated vacuum cleaners was shown, from archives dating back to the 1960s. In addition to ways in which #AI is now being studied or applied, future opportunities for using #AI were also postulated, for example for coronary artery disease, since stress tests are not 100% sensitive and the gold standard coronary angiography is invasive. #AI could help stratify patients who needed versus did not need the invasive procedure for recurrent convincing symptoms in the absence of a positive stress test. Of course, coronary CT angiography could help fill this gap, but #AI might assist with decision-making sooner.

There have been studies on #AI in Cardiology, and studies on #AI in Oncology, and at least one study in #AI in Cardio-Oncology – a study I predicted; one that is quite intuitive and mentioned above. I propose that we continue to apply #AI in Cardio-Oncology, so that the field can catch up with the rest of Cardiology and Oncology, and help us continue to develop this emergent and burgeoning multidisciplinary subspecialty.

This is an exciting time for me to be alive. I am an early adopter of artificial intelligence. I look forward to seeing more and more the availability of #AI to enhance our use of electrocardiography, echocardiography, wearables, biosensors, voice analysis, and more in Cardiology, and particularly in Cardio-Oncology, with an emphasis on primary and primordial prevention even before secondary and tertiary prevention in the area of Preventive Cardio-Oncology, and especially in women.