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Artificial Intelligence and Robotics in General Surgery: Opportunities and Challenges.
Sepehr Seifi1, Hadi Sahrai2, Negin Safari Dehnavi3
1Department of Surgery, Arkansas College of Osteopathic Medicine, Fort Smith, USA.
Cureus
|June 22, 2026
Summary
Artificial intelligence (AI) and robotics are transforming surgery from planning to recovery. While AI enhances preoperative risk prediction and intraoperative precision, challenges like data bias and regulatory hurdles remain for full adoption.
Area of Science:
- Surgical Innovation
- Medical Technology
- Artificial Intelligence in Medicine
Background:
- Robotic surgery systems have evolved significantly from early teleoperated designs to sophisticated platforms like da Vinci Xi and SP.
- Artificial intelligence (AI) is increasingly integrated into various stages of the surgical process, from preoperative planning to postoperative care.
Purpose of the Study:
- To provide a narrative review of the historical development and current applications of robotics and AI in general surgery.
- To examine the impact of AI across the entire perioperative continuum: preoperative, intraoperative, and postoperative phases.
- To identify current challenges and future directions for AI and robotic integration in surgical practice.
Main Methods:
- This review synthesitsizes existing literature on surgical robotics and AI applications in surgery.
- It traces the historical trajectory of surgical robotics and analyzes current AI implementations in different surgical phases.
- Key AI applications discussed include machine learning for risk prediction, deep learning for imaging, AI for intraoperative task automation, and AI in postoperative monitoring.
Main Results:
- AI models demonstrate superior prediction of postoperative complications compared to traditional scores.
- Deep learning enhances diagnostic accuracy in medical imaging (CT, MRI) for tumor detection and surgical planning.
- Intraoperative AI tools improve procedural step recognition (85-95% accuracy), safety assessments, and robotic system stability.
- Postoperative AI facilitates early complication detection (e.g., sepsis, DVT) and refines personalized care protocols.
Conclusions:
- AI currently serves as a crucial decision-support tool in surgery, not an autonomous agent.
- Widespread clinical adoption necessitates addressing challenges such as dataset bias, model interpretability, data privacy, and regulatory frameworks.
- Future advancements require multi-institutional data, enhanced model transparency, and clear regulatory guidelines for AI in surgery.
