arXiv:2606.30657v1 Announce Type: cross Abstract: Surgical outcomes depend not only on patient factors and postoperative care but are also strongly influenced by the quality of the operation itself. Yet, for much of mod-ern surgery, intraoperative quality has been assessed indirectly through outcomes and operative reports. The increase in minimally invasive procedures inherently guided by endoscopic video, together with advances in artificial intelligence, creates an unprecedented opportunity to systematically observe, measure, and improve surgi-cal care. This chapter introduces AI-enabled Sur
Advances in artificial intelligence and the proliferation of minimally invasive surgical procedures with endoscopic video create a timely opportunity for AI to analyze intraoperative quality.
Improving surgical outcomes through AI-driven quality assurance can significantly reduce healthcare costs, enhance patient safety, and standardize surgical excellence across institutions.
Surgical quality assessment moves from indirect measures to direct, systematic observation and analysis powered by AI, enabling continuous improvement in operative techniques.
- · Surgical AI developers
- · Healthcare systems
- · Patients
- · Medical device manufacturers
- · Medical malpractice insurers (potentially lower payouts)
- · Traditional surgical quality assessment methodologies
AI-powered systems will be integrated into operating rooms to monitor and provide real-time feedback on surgical performance.
Standardization of best practices in surgery will accelerate, potentially leading to fewer complications and improved training models.
The role of the surgeon may evolve to include closer collaboration with AI systems, leading to new certifications and training requirements for AI-assisted surgery.
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Read at arXiv cs.AI