Beyond watching: Harnessing laparoscopy to increase medical students’ engagement with robotic procedures

Barnes, Katherine; Brian, Riley; Greenberg, Anya L.; Watanaskul, Sarah; Kim, Ki Dong; O’Sullivan, Patricia; Chern, Hueylan

doi:10.1016/j.amjsurg.2023.02.024

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…However, the actual training of assistants varies [3,6]. To expedite assistant education, some authors have piloted courses focusing on a combination of didactic and psychomotor training [8,9]. Another simulation-based report found that an augmented reality intervention with 3D vision decreased the time and errors in performing complex bedside tasks [10].…”

Section: Introductionmentioning

confidence: 99%

Experience matters for robotic assistance: an analysis of case data

Brian

Ifuku

et al. 2023

J Robotic Surg

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Many robotic procedures require active participation by assistants. Most prior work on assistants’ effect on outcomes has been limited in procedural focus and scope, with studies reporting differing results. Knowing how assistant experience affects operating room time could inform operating room case scheduling and provide an impetus for additional assistant training. As such, this retrospective cohort study aimed to determine the association between assistant experience and operating room time for 2291 robotic-assisted operations performed from 2016 to 2022 at our institution. Linear regression showed a significant association between the presence of a junior resident and increased case length differential with an increase of 26.9 min (p = 0.01). There were no significant associations between the presence of a senior resident (p = 0.52), presence of a fellow (p = 0.20), or presence of a physician assistant (p = 0.43) and case length differential. The finding of increased operating room time in the presence of a junior resident during robotic cases supports consideration of the adoption of formal assistant training programs for residents to improve efficiency.

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Section: Introductionmentioning

confidence: 99%

Experience matters for robotic assistance: an analysis of case data

Brian

Ifuku

et al. 2023

J Robotic Surg

Self Cite

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“…A next step for video labeling could be incorporating real-time video labeling on an intra-operative observer screen. Though research must address the effects of this on workflow, such an intervention could allow for more efficacious learning for trainees who are relegated in parts or all of an operation to observation [53,54]. Other work could investigate automated case logging based on video labeling to allow surgeons and trainees to track their progression over time and divide joint case metrics by operator [42].…”

Section: Recommendationsmentioning

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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“…24 Barnes et al reported that trainees find value in completing laparoscopic training prior to engaging with robotic surgery platforms, as it increases their comfort with hand motions, instrument manipulation, orientation relative to the camera, and understanding of surgical engagement. 36 As such, financially limited institutions could consider adding laparoscopic training to their current models, as this technology is relatively accessible and low-cost compared with robotic simulation training.…”

Section: Component 2: Basic Skill Acquisition Through Simulationmentioning

confidence: 99%

Robotic Plastic Surgery Education: Developing a Robotic Surgery Training Program Specific to Plastic Surgery Trainees

et al. 2023

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Over the past two decades, the surgical community has increasingly embraced robotic-assisted surgery (RAS) due to its potential to enhance accuracy and decrease surgical morbidity. Plastic surgery as a field has been historically slow to incorporate RAS, with lack of adequate training posing as one of the most commonly cited barriers. To date, robot technology has been utilized for various reconstructive procedures including flap elevation and inset, pedicle dissection, and microvascular anastomosis. As RAS continues to integrate within plastic surgery procedures, the need for a structured RAS curriculum designed for plastic surgery trainees is rising. This article delineates the essential components of a plastic surgery-specific RAS curriculum and outlines current training models and assessment tools utilized across surgical subspecialties to date.

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Beyond watching: Harnessing laparoscopy to increase medical students’ engagement with robotic procedures

Cited by 4 publications

References 7 publications

Experience matters for robotic assistance: an analysis of case data

Experience matters for robotic assistance: an analysis of case data

Artificial intelligence and robotic surgical education

Robotic Plastic Surgery Education: Developing a Robotic Surgery Training Program Specific to Plastic Surgery Trainees

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