Buzzword Alert! Artificial Intelligence – Just the Hype Man or a Genuine Showstopper?

Conversations of the utility and promise of machine learning (ML) and artificial intelligence (AI) permeate all fields of medicine, and cardiology is no exception. A quick search shows that 69 posters containing the keywords “machine learning” made it into AHA’s Scientific Sessions 2020. But is it for real? Will we really see a future in with ML/AI factors into all aspects of clinical care and in fact, re-write the script on how we care for patients?

Below is some of the discussion points and imperatives that stood out to me today from the “Hope or Hype? Artificial intelligence and Machine Learning in Imaging.” session at #AHA20 featuring thought leaders Drs. Marielle Scherrer-Crosbie, Alex Bratt, David Ouyang, Tessa Cook, Damini Dey, David Playford, and Geoffrey Rubin.

  1. While awe-inspiring in its ability to make inferences and predictions human beings often cannot themselves, we must be aware that ML/AI algorithms can recreate and reinforce the bias pre-existing in our society. We must fight this by knowing it is a possibility, screening for it, and training algorithms on datasets that are truly representative. As much of the political landscape and national conversation right now centers on structural racism and bias in America, it’s is prudent to understand how the models we create can perpetuate this.
  1. Separate low hanging fruit from the unrealistic (at the moment) and consider the unrealistic tasks in the realm of discovery science. A quick rule of thumb provided by Dr. Ouyang, summarizing the words of Dr. Andrew Ng, first determines if it is possible for humans to do a task relatively quickly. If it is, we can probably automate it with AI now or in the near future.

  1. Scrutinize our data. How much do we trust it? High-quality data for ML/AI means broad, accurate, and plentiful. We need robust training labels, as free from subjectively as possible.
  2. How open is our data for inspection? Fields in computer science are far further along than medicine in deploying and improving ML/AI models because of open data sets and shared code, allowing groups to verify, tinker, and re-create to move the needle forward. Medical AI has not been so forthcoming.

  1. As new technology is rapidly evolving and making it into the clinical space, we need to be responsible for mistakes. This means we need to assess not only our model performance before deploying but also the consequences of using the model in real life. This may require RCTs and to consider ML/AI algorithms like we consider new therapeutics.

  1. What we really want is the AI running in the background saying “Hey, this task was automated and is now solved for you. Proceed as you see fit.” Humans and machines are in this shared space. The more we can integrate ML/AI to help us with tasks we are already doing, the better our results will be.

So where does this leave us? Most in our field believe ML/AI will play an important role in our future. Ideally, we will do it in a way that will make sure human intelligence is always paired with artificial intelligence to create a product neither of the two could be alone, will ensure our algorithms are free of bias and openly shared to allow for continuous improvement.


“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.”


What Do We Know About the Future? The Digital Health Era

What do we know about the future? Although millions of possibilities come into mind, one thing is certain. One way or another, our lives are more and more dependent on computers and social media networks. How many of you check on your smartwatch or social media feeds more than once a day? I, for instance, am occasionally obsessed with my heart rate measurements and sleep patterns and constantly try to get a better understanding on how to optimize my own health. It’s very easy to get lost in trying to find the right kind of research from scientific journals. Most of the time people turn to social media to get ideas to make a healthcare decision. Study shows that 80% of internet users are looking specifically for health information1.

In today’s American Heart Association Scientific Sessions, a group of pioneers shares their insights in novel technologies for arrhythmia detection2 using big data to manage patient care systems. Dr. Leslie Saxon, of the University of Southern California Center for Body Computing, discussed the advancements of digital health, such as increased diversity of computer monitoring devices, increased data accessibility via the cloud, and novel digital biomarker identification. Particularly, using remote device follow-up improved 30-40% survival rate of patients after cardiac defibrillator implantation, according to a published clinical study (the ALTITUTE survival study)3. Another highlight from Dr. Leslie’s research, CORA, is a patient-facing, manufacturer-agnostic mobile application. CORA can help improve communications between patients and caregivers, visualize complex data in a simple way, and educate patients and caregivers about their health conditions.

Other advances in finding software solutions driven by big data collection are also critical in this digital era. An ongoing clinical study to determine if the Apple Watch and a heart health program can improve heart health outcomes, HEARTLINE, are recently launched in Feb 2020 with a collaboration between Johnson& Johnson and Apple (Clinical Trial NCT04276441).

Dr. Marco V. Perez from Stanford University talked out the recent developments of patient-acquired wearable technology, such as devices to monitor blood oxygen levels, glucose levels, and sleep rhythm. One of the challenges is potential data overload. Dr. Perez’s team implemented a machine learning algorism using a convolutional neural network to investigate 1.5 million ECG graphs from 500,000 patients collected from wearable devices. This artificial intelligence approach opens a new window with many possibilities in the health care systems and address novel research problems. Dr. Khaldoun G. Tarakji from Cleveland Clinic discussed how to use wearable devices to detect atrial fibrillation from a clinical practice perspective. He presented several case studies on using Apple watch to help diagnose and manage atrial fibrillation. In the field of telemedicine, Dr. Tarakji mentioned the advantages of using wearable devices to conduct virtual visits to improve patient care outcomes.

Figure 1: New technologies for the detection of atrial fibrillation 2

Despite apparent advantages of the application of wearable devices in the health care system, Dr. Paul D. Varosy from the University of Colorado discussed the challenges of using wearable devices regarding clinical, legal, cybersecurity, and ethical implications. The main questions are: How to fit data management into busy clinical practice? How to maintain financial sustainability? How to improve cybersecurity vulnerability? How to handle potential oversight? And who owns the data? These questions require continuing efforts from policy workers, researchers, doctors, and patients to work together to find solutions.

The new kid on the block: social media in the health care system. Dr. Janet K. Han from UCLA talked about the possibility of using social media to transform arrhythmia health care. Social media can make health information more accessible, engage patients better, provide valuable social and emotional supports4. Combining social media with big data with artificial intelligence and machine learning provides faster diagnosis and management5.

Wearable devices in combination with big data analyses in healthcare practices have a promising future. They are more accessible, engaging, and high payoff. Despite potential challenges, the era of digital health presents many possibilities and advantages in patients’ healthcare outcomes.


  1. Fox S. Profiles of Health Information Seekers. Pew Internet & American Life Project. 2011.
  2. Zungsontiporn N, Link MS. Newer technologies for detection of atrial fibrillation. BMJ (Online). 2018.
  3. Saxon LA, Hayes DL, Gilliam FR, Heidenreich PA, Day J, Seth M, Meyer TE, Jones PW, Boehmer JP. Long-term outcome after ICD and CRT implantation and influence of remote device follow-up: The ALTITUDE survival study. Circulation. 2010.
  4. Hawkins CM, DeLaO AJ, Hung C. Social Media and the Patient Experience. Journal of the American College of Radiology. 2016.
  5. Simonsen L, Gog JR, Olson D, Viboud C. Infectious disease surveillance in the big data era: Towards faster and locally relevant systems. Journal of Infectious Diseases. 2016.


“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.”


Tech in Cardiology

Tech in Cardiology

On a recent flight from San Francisco, I found myself sitting in a dreaded middle seat.  To my left was a programmer typing way in Python, and to my right was an oncologist flipping through a slide set on chemotherapy trials.  While this may sound like the beginning of a bad joke, I remember this moment because it got me thinking about the influence of tech on medicine.  The purpose of my trip, by the way, was to interview for a fellowship position in cardiology, a specialty with arguably some of the most impressive tech.



Not to discount advances in medical devices (e.g. leadless pacemakers, bioprosthetic valves), the emergence of consumer-facing wearable devices is as trendy as ever.  Google recently collaborated with AHA to build its fitness app (Google Fit), which uses algorithms to quantify physical activity in terms of “heart points.”1  The Apple Health app now incorporates EKG capabilities, allowing patients to record episodes of arrhythmias—something I have certainly witnessed in cardiology clinic.2


Big data

Big data is an increasingly prominent component of clinical research, and a number of joint ventures with medical and tech leaders have emerged.  One Brave Idea3 is a research collaboration between AHA and Verily (Alphabet’s life sciences division) which uses genomics to study coronary artery disease.  Meanwhile, Verily’s Project Baseline4 is a massive longitudinal observational study—a modern version of the Framingham Heart Study.


Artificial intelligence

AI could eventually play a prominent role in medical diagnosis and decision-making.  The Stanford Machine Learning Group5 has developed a neural network that outperforms cardiologists in diagnosing arrhythmias on EKG—a significant improvement on existing algorithms which are often unreliable.  AI also carries vast potential in radiologic interpretation.  Already, Veril is using machine learning to interpret retinal images not only to detect diabetic retinopathy and macular edema but also to extrapolate information about cardiovascular risk.6



Electronic medical records represent an obvious space for tech innovation.  Fast Healthcare Interoperability Resources (FHIR) are making it easier to share health information across our disjointed EMR systems.  Providers are now able to push health data directly to patients’ iPhones using Apple Health Records.7  One can only speculate whether we will see a legacy software giant compete directly in the EMR space.


Cardiology and the rest of medicine has long excelled at basic science and translational research, but digital tech is increasingly creeping in.  We are in a tech zeitgeist, and this is good for both patients and providers.



  1. https://www.heart.org/en/news/2018/08/21/google-just-launched-heart-points-here-are-5-things-you-need-to-know
  2. https://www.apple.com/healthcare/site/docs/Apple_Watch_Arrhythmia_Detection.pdf
  3. https://www.onebraveidea.org/
  4. https://verily.com/projects/precision-medicine/baseline-study/
  5. https://stanfordmlgroup.github.io/projects/ecg/
  6. https://blog.verily.com/2018/02/eyes-window-into-heart-health.htm
  7. https://www.apple.com/healthcare/health-records/

Deep Learning in Cardiology

Thirteen years ago in my first anatomy class of Med School, the instructor asked us to make sure our learning is “deep.”

“You need to memorize the names of every single tiny nerve and muscle, because they all will be on your exam. One day you could be surgeons and if you cut out the wrong structure, you can kill someone!” he asserted as we all stood there in fear.

Later in Med School, we were told that half of what we’re learning will be wrong by the time we practice. But one thing we were not told is that the way we learn and the entire premise of what makes a good doctor would also change. For example, “deep learning” itself now means something different to me and to most healthcare professionals. If you are reading this article so far, then you are likely to have seen the term floating around in medical journals.

Deep learning is a type of machine learning in which the computer uses multiple layers of processing to extract features from otherwise vague data input, such as an ECG or a slice of an MRI.  Each layer uses outputs from the previous layer. Through deep learning, the computer simulates the neural network of the brain and is able to learn and make sense of abstraction.

Working at the Broad Institute of Harvard and MIT allowed me to recently be part of a team that uses deep learning to solve important problems in cardiology.  Over several weeks, cardiologists, scientists, and machine learning experts worked in teams to train computers on deep learning models so that they understand data such as medications, ECGs, genomic architecture, and cardiac MRIs of tens to hundreds of thousands of people.

The insights I gained were incredible.  Just like medical students perform better on their exam if they learn “deeper,” the longer you train a computer model, the more it learns and the better it performs in predicting – but it does so at much faster rates than any doctor could ever match. For example, in only two days, we trained a computer to read the ejection fraction from a cardiac MRI as good as a doctor would. Using the MRI, the computer could also predict with reasonable precision the presence of hypertension and coronary artery disease, without knowing anything else about the patient. The power of computer vision is beyond imagination. While it could take you a full day to read 100 ECGs, a well-trained deep learning model could read them in only few seconds. It could also identify patterns in the data that the human eye could not discern, which might or might not be biologically or clinically relevant.

As data availability and computing power continue to grow, we will be seeing more and more applications of deep learning in cardiology.  While we do so, we should stay mindful that human supervision and our role as doctors in charge of our patient’s health is more important than ever. This requires us to understand how computers work and how those models are built through working with multidisciplinary teams. If we do this right, we can probably do less deep learning ourselves by delegating to computers, and gain a whole lot of extra time that we can invest in taking care of our patients.



New Technologies in the Health Innovation Pavilion

This morning I strayed from my usual hangout in the basic sciences sessions to investigate the hottest new products in the Health Innovation Pavilion at the Health Tech Competition. In this event, 8 highly talented applicant companies were allotted 3 minutes to pitch their company or product, followed by 5 minutes of Q&A from a panel of venture capitalists and AHA VIPs. Contestants were scored on novelty, innovation, potential patient outcomes, ability to address patient and provider needs, and strategy to launch and sell, among other criteria.

A focus emerged early in the competition: data aggregation. We’re seeing start-up companies developing digital platforms that collect massive amounts of patient data and process it to improve cardiovascular health outcomes. The target consumer varied from individual to hospital system, and the aims and applications stretched from prevention to detection and diagnosis. I particularly enjoyed hearing about Seqster’s software that integrates health records, DNA and fitness data in one place, though I thought all the panelists in the “real-life Shark Tank” had interesting and educational pitches.

The judges’ deliberations illuminated potential advances and pitfalls facing the field, and I think we need to ask ourselves a few things as both scientists and consumers. How might we respond if an adverse event is detected, and what are the consequences if something is missed? How efficient and accurate are the technologies? Who owns the data?

The pitch competition today demonstrated that health technologies hold great potential. It’s clear that as our tools evolve to improve patient health, the direction and guidance provided by our congregated cardiovascular experts, like those at Session 2018, will be invaluable.


Annie Roessler Headshot

Annie Roessler is a PhD Candidate at Loyola University in Chicago, IL. Her research focuses on the neurobiology and molecular mechanisms of electrically-induced cardioprotection. She tweets @ThePilotStudy and blogs at flaskhalffull.com