The Experienced Surgeon and New Tricks—It’s Time for Full Adoption and Support of Automated Performance Metrics and Databases

Pugh, Carla M.

doi:10.1001/jamasurg.2021.4531

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Feedback is a second major area in which AI has been studied in robotic surgical education. Given surgeons' inconsistent formative feedback that may not be actionable for learners, AI offers a mechanism by which reliable, high-quality feedback could be provided in robotic surgery [43]. The first way by which AI could improve feedback is by making meaning of APMs.…”

Section: Feedbackmentioning

confidence: 99%

Artificial intelligence and robotic surgical education

Brian,

Murillo,

Gomes

et al. 2024

Global Surg Educ

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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.

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Section: Feedbackmentioning

confidence: 99%