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Introduction <p>To examine the current landscape of artificial intelligence (AI) applications in vitreoretinal (VR) diseases and surgery, with the aim of identifying knowledge gaps and guiding future directions in this rapidly evolving field.</p> Methods <p>Systematic review including original studies involving the use of AI and focusing on VR pathologies. A comprehensive electronic search of the literature was carried out in multiple databases.</p> Results <p>Thirty-seven studies were included. Most evaluated machine learning or deep learning models for preoperative prognostication using optical coherence tomography with or without clinical variables. Predictive performance for postoperative best-corrected visual acuity (BCVA) was high in several cohorts (<i>R</i><sup>2</sup> up to 0.80; area under the receiver operating characteristic curve [AUROC] &gt; 0.95), with models consistently highlighting outer retinal biomarkers as key determinants of visual recovery after epiretinal membrane and macular hole surgery. For anatomical outcomes, deep learning models frequently achieved &gt; 90% accuracy in predicting macular hole closure and retinal reattachment/reattachment-related endpoints. Intraoperative computer-vision systems demonstrated feasibility for real-time instrument detection and tracking, reporting precision above 90% in experimental or early clinical settings. Large language models showed moderate-to-high agreement with expert surgical planning (80–93%) and potential utility in education and workflow support; however, across domains, most studies were retrospective and single-center, with limited external validation.</p> Conclusions <p>AI may transform vitreoretinal surgery, from outcome prediction to intraoperative guidance and workflow support. Despite strong performance in research settings, broader clinical adoption requires prospective validation to ensure reliability, transparency, and real-world benefit.</p>

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Artificial Intelligence in Vitreoretinal Surgery: A Systematic Review of Current Applications and Future Directions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月7日

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  • Enrico Bernardi,
  • Lorenzo Ferro Desideri,
  • Neil Shah,
  • Carla Troyas,
  • Ben Kirkpatrick,
  • Yousif Subhi,
  • Martin Zinkernagel,
  • Rodrigo Anguita

摘要

Introduction

To examine the current landscape of artificial intelligence (AI) applications in vitreoretinal (VR) diseases and surgery, with the aim of identifying knowledge gaps and guiding future directions in this rapidly evolving field.

Methods

Systematic review including original studies involving the use of AI and focusing on VR pathologies. A comprehensive electronic search of the literature was carried out in multiple databases.

Results

Thirty-seven studies were included. Most evaluated machine learning or deep learning models for preoperative prognostication using optical coherence tomography with or without clinical variables. Predictive performance for postoperative best-corrected visual acuity (BCVA) was high in several cohorts (R2 up to 0.80; area under the receiver operating characteristic curve [AUROC] > 0.95), with models consistently highlighting outer retinal biomarkers as key determinants of visual recovery after epiretinal membrane and macular hole surgery. For anatomical outcomes, deep learning models frequently achieved > 90% accuracy in predicting macular hole closure and retinal reattachment/reattachment-related endpoints. Intraoperative computer-vision systems demonstrated feasibility for real-time instrument detection and tracking, reporting precision above 90% in experimental or early clinical settings. Large language models showed moderate-to-high agreement with expert surgical planning (80–93%) and potential utility in education and workflow support; however, across domains, most studies were retrospective and single-center, with limited external validation.

Conclusions

AI may transform vitreoretinal surgery, from outcome prediction to intraoperative guidance and workflow support. Despite strong performance in research settings, broader clinical adoption requires prospective validation to ensure reliability, transparency, and real-world benefit.