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      Attitudes of the Surgical Team Toward Artificial Intelligence in Neurosurgery: International 2-Stage Cross-Sectional Survey

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          Abstract

          Background

          Artificial intelligence (AI) has the potential to disrupt how we diagnose and treat patients. Previous work by our group has demonstrated that the majority of patients and their relatives feel comfortable with the application of AI to augment surgical care. The aim of this study was to similarly evaluate the attitudes of surgeons and the wider surgical team toward the role of AI in neurosurgery.

          Methods

          In a 2-stage cross sectional survey, an initial open-question qualitative survey was created to determine the perspective of the surgical team on AI in neurosurgery including surgeons, anesthetists, nurses, and operating room practitioners. Thematic analysis was performed to develop a second-stage quantitative survey that was distributed via social media. We assessed the extent to which they agreed and were comfortable with real-world AI implementation using a 5-point Likert scale.

          Results

          In the first-stage survey, 33 participants responded. Six main themes were identified: imaging interpretation and preoperative diagnosis, coordination of the surgical team, operative planning, real-time alert of hazards and complications, autonomous surgery, and postoperative management and follow-up. In the second stage, 100 participants responded. Responders somewhat agreed or strongly agreed about AI being used for imaging interpretation (62%), operative planning (82%), coordination of the surgical team (70%), real-time alert of hazards and complications (85%), and autonomous surgery (66%). The role of AI within postoperative management and follow-up was less agreeable (49%).

          Conclusions

          This survey highlights that the majority of surgeons and the wider surgical team both agree and are comfortable with the application of AI within neurosurgery.

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          Most cited references31

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          Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.

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            The practical implementation of artificial intelligence technologies in medicine

            The development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability across multiple platforms, and concern for patient safety. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China.
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              Good practice in the conduct and reporting of survey research.

              K. Kelley (2003)
              Survey research is sometimes regarded as an easy research approach. However, as with any other research approach and method, it is easy to conduct a survey of poor quality rather than one of high quality and real value. This paper provides a checklist of good practice in the conduct and reporting of survey research. Its purpose is to assist the novice researcher to produce survey work to a high standard, meaning a standard at which the results will be regarded as credible. The paper first provides an overview of the approach and then guides the reader step-by-step through the processes of data collection, data analysis, and reporting. It is not intended to provide a manual of how to conduct a survey, but rather to identify common pitfalls and oversights to be avoided by researchers if their work is to be valid and credible.
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                Author and article information

                Contributors
                Journal
                World Neurosurg
                World Neurosurg
                World Neurosurgery
                Elsevier
                1878-8750
                1878-8769
                1 February 2021
                February 2021
                : 146
                : e724-e730
                Affiliations
                [1 ]Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, University College, London, United Kingdom
                [2 ]Wellcome EPSRC Centre for Interventional and Surgical Sciences, University College, London, United Kingdom
                [3 ]Department of Neurosurgery, Policlinico Gaspare Rodolico, Catania, Italy
                Author notes
                []To whom correspondence should be addressed: Hugo Layard Horsfall, M.B.B.S. Hugo.layardhorsfall@ 123456ucl.ac.uk
                Article
                S1878-8750(20)32373-1
                10.1016/j.wneu.2020.10.171
                7910281
                33248306
                85054369-e30e-47d3-accd-56fb9d46fb42
                © 2020 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 7 October 2020
                : 31 October 2020
                Categories
                Original Article

                artificial intelligence,machine learning,neurosurgery,operative planning,survey

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