Artificial intelligence in pediatric cardiology and cardiac surgery: Irrational hype or paradigm shift?

摘要

“Healthcare is an information industry that continues to think that it is a biological industry.” – Laurence McMahon at the AAHC Thought Leadership Institute meeting, August, 2016. With the current imbroglio of health care and burden of work, some of us have been bereft of the pure joys of being a physician or caretaker in pediatric cardiology. Now imagine your future experience as a practitioner for children with heart disease in the year 2040: You are in the serene cardiac intensive care pod where real-time analytics are displayed (rather than the de rigueur vital signs) and deep learning (in the form of recurrent neural network) are now routinely used for personalized intensive care unit decision support and to mitigate the stress of families and physicians/nurses. There is no longer formal lengthy AM rounds as communication among team members is now continuous. The old electronic record and computers are all no longer omnipresent, and patient information is displayed on the video wall only upon activation or automatically in the case of sudden changes. The conversations are recorded with a tiny microphone on your shirt and analyzed with natural language processing (NLP) in order to capture key information onto the electronic record. You receive notifications about recent advances in diagnostics and therapy that pertain to your patients, and you read these new notifications and answer questions to gain points for individualized continuing medical education (as board examinations have been eliminated finally). You then go to the outpatient area. Several of your patients are triaged to be seen remotely with telepresence. A new child with heart failure is seen after the admission process is completed automatically with robotic process automation, and the echocardiogram is preliminarily read, aided by computer vision (by the use of convoluted neural network [CNN]). A precision medicine protocol is promoted by individualized therapy options, and pharmacotherapy is accompanied by the pharmacogenomic profile. A cognitive architecture based on heart failure experts from numerous centers, published reports in this disease condition, as well as the accumulated data from the entire pediatric heart failure patient cohort is utilized for best clinical decision for this child. You use augmented reality to explain the heart condition to the parents including illustration of a micro-axial device for ventricular support (as orthotropic transplantation has been supplanted by advances in miniaturized support devices and nanomedicine). The patient is then set up for wearable technology with embedded artificial intelligence (AI) to monitor her blood pressure and heart rate while she is on new medical regimen at home. For a subspecialty that is particularly rich with imaging and clinical data already, and with more sources of data to come in the very near future (especially with electrocardiogram [EKG or ECG] apps, implantable monitors, and biosensors), pediatric cardiology remains relatively dormant in this burgeoning domain of AI.[1] A comprehensive review by Johnson et al. discusses the many dimensions how AI can affect cardiology: from research to clinical practice and even population health.[2] This review also well delineated the basics of machine learning (supervised/unsupervised learning and even the abstruse concept of reinforcement learning). Another excellent and concise review by Shameer et al. focuses on the aspects of cardiovascular medicine in the context of machine learning.[3] Finally, there is an excellent review that showed how effective AI can be in the context of precision cardiovascular medicine, an aspect of cardiology that will be the cornerstone of pediatric cardiology in the near future.[4] First, there is relatively increased use of AI and data science in the domain of cardiac imaging ranging from ECG to echocardiography for both diagnosis and prognosis. The major contribution to medical image interpretation has been C