Editing AI in Surgery (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Surgical AI Application Status
! Application !! AI Performance !! Clinical Deployment !! Key Barrier
|-
| Phase recognition (cholecystectomy) || >90% accuracy || Research → early commercial || Dataset annotation cost
|-
| Instrument detection || 85-95% mAP || Research || Occlusion, bleeding
|-
| CVS assessment || Moderate (70-80%) || Early commercial || Generalization across surgeons
|-
| Outcome prediction (NSQIP) || C-stat 0.85-0.95 || Deployed (web calculator) || EHR integration
|-
| Skill assessment || Moderate || Research/training programs || Ground truth subjectivity
|-
| Autonomous suturing || Demonstrated (preclinical) || Animal models only || Regulatory, safety
|}

'''Failure modes''': Blood and smoke obscuring the operative field causes computer vision failures. Domain shift across different hospital setups, camera systems, and surgeon styles. Real-time latency requirements for intraoperative AI (must be <1 second). Liability ambiguity when AI-assisted surgery leads to complications. Over-reliance on AI recommendations causing surgeons to ignore their own clinical judgment.
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