# Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes

## TL;DR

AI is transforming adult cardiovascular medicine and surgery by improving diagnostics, surgical planning, and postoperative care, though challenges remain in validation and implementation.

## Contribution

The paper highlights real-world AI deployments in cardiac surgery and their clinical outcomes, emphasizing the shift toward predictive and personalized care.

## Key findings

- AI improves preoperative diagnostics and risk assessment using ECGs, CT/MRI, and echocardiography.
- Intraoperative AI tools enhance robotic precision and hemodynamic monitoring, reducing complications.
- Postoperative machine-learning systems predict complications hours before clinical signs appear.

## Abstract

Artificial intelligence (AI) is rapidly reshaping adult cardiac surgery, enabling more accurate diagnostics, personalized risk assessment, advanced surgical planning, and proactive postoperative care. Preoperatively, deep-learning interpretation of ECGs, automated CT/MRI segmentation, and video-based echocardiography improve early disease detection and refine risk stratification beyond conventional tools such as EuroSCORE II and the STS calculator. AI-driven 3D reconstruction, virtual simulation, and augmented-reality platforms enhance planning for structural heart and aortic procedures by optimizing device selection and anticipating complications. Intraoperatively, AI augments robotic precision, stabilizes instrument motion, identifies anatomy through computer vision, and predicts hemodynamic instability via real-time waveform analytics. Integration of the Hypotension Prediction Index into perioperative pathways has already demonstrated reductions in ventilation duration and improved hemodynamic control. Postoperatively, machine-learning early-warning systems and physiologic waveform models predict acute kidney injury, low-cardiac-output syndrome, respiratory failure, and sepsis hours before clinical deterioration, while emerging closed-loop control and remote monitoring tools extend individualized management into the recovery phase. Despite these advances, current evidence is limited by retrospective study designs, heterogeneous datasets, variable transparency, and regulatory and workflow barriers. Nonetheless, rapid progress in multimodal foundation models, digital twins, hybrid OR ecosystems, and semi-autonomous robotics signals a transition toward increasingly precise, predictive, and personalized cardiac surgical care. With rigorous validation and thoughtful implementation, AI has the potential to substantially improve safety, decision-making, and outcomes across the entire cardiac surgical continuum.