Artificial intelligence and cardiac surgery during COVID‐19 era

Abstract

The coronavirus disease 2019 (COVID‐19) pandemic has increased the burden on hospital staff world‐wide. Through the redistribution of scarce resources to these high‐priority cases, the cardiac sector has fallen behind. In efforts to reduce transmission, reduction in direct patient–physician contact has led to a backlog of cardiac cases. However, this accumulation of postponed or cancelled nonurgent cardiac care seems to be resolvable with the assistance of technology. From telemedicine to artificial intelligence (AI), technology has transformed healthcare systems nationwide. Telemedicine enables patient monitoring from a distance, while AI unveils a whole new realm of possibilities in clinical practice, examples include: traditional systems replacement with more efficient and accurate processing machines; automation of clerical process; and triage assistance through risk predictions. These possibilities are driven by deep and machine learning. The two subsets of AI are explored and limitations regarding “big data” are discussed. The aims of this review are to explore AI: the advancements in methodology; current integration in cardiac surgery or other clinical scenarios; and potential future roles, which are innately nearing as the COVID‐19 era urges alternative approaches for care.

The Development of Electronic Health and Artificial Intelligence in Surgery after the SARS-CoV-2 Pandemic—A Scoping Review

“…Additionally, Bhaskar et al proclaimed that the association between eHealth and artificial intelligence is crucial to understand as it will help practitioners establish methods of dealing with future pandemics [ 16 ]. Similarly, Khalsa et al stated that the adoption of electronic health in the cardiac surgical domain had created room for integrating artificial intelligence into the sector [ 17 ]. However, the authors pointed out that the incorporation of AI in surgery has numerous drawbacks primarily associated with patient safety [ 17 ].…”

“…Similarly, Khalsa et al stated that the adoption of electronic health in the cardiac surgical domain had created room for integrating artificial intelligence into the sector [ 17 ]. However, the authors pointed out that the incorporation of AI in surgery has numerous drawbacks primarily associated with patient safety [ 17 ]. For instance, the association between robotic procedures and patient death has prevailed, despite arguments that physicians’ inexperience with the robots might have contributed to the occurrences.…”

The future of Cardiothoracic surgery in Artificial intelligence

“…With the advent and integration of AI into clinical care, the traditional systems are replaced with more efficient and more accurate systems. Using deep and machine learning, AI helps in the pre-operative automation of clerical processes, provides assistance in how patients can be triaged and gives risk predictions during the COVID-19 pandemic where resources were already scarce, and cardiac surgery patients were facing delays in delivery of care [ 26 ]. Using machine learning techniques, Yoon et al devised a new personalized method for prediction of risk both pre- and post-cardiac transplantation.…”

Big Data in cardiac surgery: real world and perspectives

“…DL could be a module to automate predictive analysis, from which data is deduced in a non-linear way. The advantage of a non-linear interpretation is the better ability to identify and interpret more complex characteristics [ 24 ] and therefore is linked to a hierarchy of increasing complexity and abstraction [ 25 ]. DL is used for image evaluation, such as cardiac magnetic resonance scans; this requires adequate skills and systems [ 26 , 27 ].…”