• Br J Surg · Jan 2024

    Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review.

    • Matthew W E Boal, Dimitrios Anastasiou, Freweini Tesfai, Walaa Ghamrawi, Evangelos Mazomenos, Nathan Curtis, Justin W Collins, Ashwin Sridhar, John Kelly, Danail Stoyanov, and Nader K Francis.
    • The Griffin Institute, Northwick Park & St Marks' Hospital, London, UK.
    • Br J Surg. 2024 Jan 3; 111 (1).

    BackgroundThere is a need to standardize training in robotic surgery, including objective assessment for accreditation. This systematic review aimed to identify objective tools for technical skills assessment, providing evaluation statuses to guide research and inform implementation into training curricula.MethodsA systematic literature search was conducted in accordance with the PRISMA guidelines. Ovid Embase/Medline, PubMed and Web of Science were searched. Inclusion criterion: robotic surgery technical skills tools. Exclusion criteria: non-technical, laparoscopy or open skills only. Manual tools and automated performance metrics (APMs) were analysed using Messick's concept of validity and the Oxford Centre of Evidence-Based Medicine (OCEBM) Levels of Evidence and Recommendation (LoR). A bespoke tool analysed artificial intelligence (AI) studies. The Modified Downs-Black checklist was used to assess risk of bias.ResultsTwo hundred and forty-seven studies were analysed, identifying: 8 global rating scales, 26 procedure-/task-specific tools, 3 main error-based methods, 10 simulators, 28 studies analysing APMs and 53 AI studies. Global Evaluative Assessment of Robotic Skills and the da Vinci Skills Simulator were the most evaluated tools at LoR 1 (OCEBM). Three procedure-specific tools, 3 error-based methods and 1 non-simulator APMs reached LoR 2. AI models estimated outcomes (skill or clinical), demonstrating superior accuracy rates in the laboratory with 60 per cent of methods reporting accuracies over 90 per cent, compared to real surgery ranging from 67 to 100 per cent.ConclusionsManual and automated assessment tools for robotic surgery are not well validated and require further evaluation before use in accreditation processes.PROSPERO: registration ID CRD42022304901.© The Author(s) 2023. Published by Oxford University Press on behalf of BJS Society Ltd.

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