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<p>Patient outcomes after robotic surgery vary widely, often reflecting differences in surgical performance. Artificial intelligence (AI) offers new ways to address this variability, with applications spanning automated skills assessment and feedback, intraoperative guidance and autonomous surgery. The most credible short-range advances of AI in this space consist in generating assistive systems that enhance perception, anticipate risks and standardize feedback while remaining under surgeon control. Results from early studies suggest that AI can influence decision-making, reduce errors and shorten learning curves, particularly in areas such as augmented navigation, anatomy recognition, error detection and telesurgery support. Long-term directions include emerging vision–language–action interfaces capable of programming task-specific support through natural language. In addition to technical performance, translation of AI into clinical practice will require robust datasets, systems designed around human users, regulatory alignment and clear accountability. Ultimately, the measure of surgical AI will be patient outcomes, including reduced complications, fast proficiency acquisition and improved outcome consistency across diverse settings.</p>

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The future of robotic surgery in the age of artificial intelligence

  • 期刊论文

  • 发表日期：

    2026年5月8日

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  • Ludovica Cella,
  • Jasmine Lin,
  • Mitchell G. Goldenberg,
  • Andrew J. Hung

摘要

Patient outcomes after robotic surgery vary widely, often reflecting differences in surgical performance. Artificial intelligence (AI) offers new ways to address this variability, with applications spanning automated skills assessment and feedback, intraoperative guidance and autonomous surgery. The most credible short-range advances of AI in this space consist in generating assistive systems that enhance perception, anticipate risks and standardize feedback while remaining under surgeon control. Results from early studies suggest that AI can influence decision-making, reduce errors and shorten learning curves, particularly in areas such as augmented navigation, anatomy recognition, error detection and telesurgery support. Long-term directions include emerging vision–language–action interfaces capable of programming task-specific support through natural language. In addition to technical performance, translation of AI into clinical practice will require robust datasets, systems designed around human users, regulatory alignment and clear accountability. Ultimately, the measure of surgical AI will be patient outcomes, including reduced complications, fast proficiency acquisition and improved outcome consistency across diverse settings.