首页 /研究 /Artificial intelligence and robotic surgical education
SURGICAL

Artificial intelligence and robotic surgical education

Riley Brian, Alyssa Murillo, Camilla Gomes, Adnan Alseidi

发表年份
2024
引用次数
23
访问权限
开放获取

摘要

Abstract There are numerous barriers in robotic surgical training, including reliance on observational learning, low-quality feedback, and inconsistent assessment. Artificial intelligence (AI) offers potential solutions to these central problems in robotic surgical education and may allow for more efficient and efficacious training. Three key areas in which AI has particular relevance to robotic surgical education are video labeling, feedback, and assessment. Video labeling refers to the automated designation of prespecified categories to operative videos. Numerous prior studies have applied AI for video labeling, particularly for retrospective educational review after an operation. Video labeling allows learners and their instructors to rapidly identify critical parts of an operative video. We recommend incorporating AI-based video labeling into robotic surgical education where available. AI also offers a mechanism by which reliable feedback can be provided in robotic surgery. Feedback through AI harnesses automated performance metrics (APMs) and natural language processing (NLP) to provide actionable and descriptive plans to learners while reducing faculty assessment burden. We recommend combining supervised AI-generated, APM-based feedback with expert-based feedback to allow surgeons and trainees to reflect on metrics like bimanual dexterity and efficiency. Finally, summative assessment by AI could allow for automated appraisal of surgeons or surgical trainees. However, AI-based assessment remains limited by concerns around bias and opaque processes. Several studies have applied computer vision to compare AI-based assessment with expert-completed rating scales, though such work remains investigational. At this time, we recommend against the use of AI for summative assessment pending additional validity evidence. Overall, AI offers solutions and promising future directions by which to address multiple educational challenges in robotic surgery. Through advances in video labeling, feedback, and assessment, AI has demonstrated ways by which to increase the efficiency and efficacy of robotic surgical education.

关键词

Summative assessmentComputer scienceArtificial intelligenceRelevance (law)Observational studyQuality (philosophy)Medical physicsFormative assessmentMedicinePsychology

相关论文

查看 SURGICAL 分类全部论文