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From innovation to implementation: Addressing the AI adoption gap in cardiac surgery.

Dabeluchi Ngwu1, Fahd Hamadi1, Oluranti Akinyemi2

  • 1Division of Thoracic Surgery, Department of Surgery, King Abdullah Hospital, Bisha, Saudi Arabia.

American Heart Journal Plus : Cardiology Research and Practice
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Artificial intelligence (AI) shows potential in cardiac surgery for diagnostics and operational efficiency but faces implementation barriers. Further research and clinician-centered strategies are needed for successful integration into clinical practice.

Keywords:
Artificial IntelligenceCardiac surgeryClinical decision supportEthical AIOperating room efficiencyPersonalized surgery

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Area of Science:

  • Cardiovascular Medicine
  • Cardiac Surgery
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) advancement in cardiovascular medicine has not translated equally into cardiac surgery.
  • Current AI applications in cardiac surgery are largely confined to research settings, necessitating a review of their clinical value and implementation challenges.

Purpose of the Study:

  • To systematically review and synthesize the current state of AI applications in cardiac surgery.
  • To identify validated clinical value and persistent barriers to AI implementation in cardiac surgical care.

Main Methods:

  • Systematic review following PRISMA 2020 guidelines.
  • Searched PubMed, Scopus, and Web of Science for studies from January 2015 to September 2024.
  • Included 45 primary studies and 8 foundational studies, synthesizing findings across diagnostic support, treatment planning, intraoperative support, operational efficiency, and equitable access.

Main Results:

  • AI shows strong performance in echocardiography interpretation, outcome prediction, and perioperative resource planning, often outperforming traditional models.
  • Computer-vision AI aids surgical phase recognition and intraoperative imaging; operational AI tools enhance scheduling, transfusion forecasting, and bed allocation.
  • Evidence for AI improving equitable care is emerging but limited; most studies are retrospective, single-center, and lack external validation or clinical integration.

Conclusions:

  • AI is poised to enhance cardiac surgery, particularly in imaging and operational logistics.
  • Key barriers to AI adoption include data heterogeneity, lack of interpretability, regulatory issues, and poor workflow integration.
  • Future progress requires multicenter data collaboration, robust validation, and clinician-centered strategies to integrate AI as an augmentative tool.