Artificial intelligence and cardiac surgery during COVID-19 era

Area of Science:

• Healthcare Technology
• Cardiology
• Artificial Intelligence

Background:

• The COVID-19 pandemic has strained hospital resources globally, leading to a backlog in non-urgent cardiac care due to reduced patient-physician contact.
• Scarce resources were reallocated to high-priority COVID-19 cases, causing the cardiac sector to fall behind.
• The accumulation of postponed cardiac procedures presents a significant challenge to healthcare systems.

Purpose of the Study:

• To explore the advancements in artificial intelligence (AI) methodology.
• To review the current integration of AI in cardiac surgery and other clinical settings.
• To discuss the potential future roles of AI in addressing healthcare challenges, particularly in the context of the COVID-19 pandemic.

Main Methods:

• Review of current literature on AI, machine learning, and telemedicine in healthcare.
• Exploration of deep learning and machine learning subsets within AI.
• Discussion of limitations related to "big data" in AI applications.

Main Results:

• Technology, including telemedicine and AI, offers solutions for managing the backlog of non-urgent cardiac care.
• AI presents opportunities for more efficient and accurate healthcare processes, including risk prediction for triage and automation of clerical tasks.
• Advancements in AI methodology and its integration into clinical practice are rapidly evolving.

Conclusions:

• AI and telemedicine are transformative technologies capable of resolving the cardiac care backlog caused by the pandemic.
• AI's potential in cardiac surgery and clinical practice is significant, driven by machine and deep learning.
• The COVID-19 pandemic accelerates the adoption of innovative technological approaches for patient care.