Surgical AI scene graph review highlights data divide, proposes new validation

A scoping review of 52 studies on scene graphs in surgery reveals a significant increase in research, with a notable shift towards foundation and generative AI models. However, a 'data divide' persists, with most research using real-world endoscopic video while external operating room modeling relies on simulations. The review proposes a new 'Validation Trinity' framework to address the gap between current computer vision metrics and the needs for clinical validation of these neuro-symbolic AI systems. AI

IMPACT Proposes a new validation framework for neuro-symbolic AI in surgery, aiming to bridge the gap to clinical practice.

RANK_REASON This is a scoping review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• Angelo Henriques
• Foundation Models
• Generative AI
• Scene Graphs

• paper
• safety
• product

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