Artificial Intelligence for Emerging Technology in Surgery: Systematic Review and Validation

Nwoye, Ephraim, Woo, Wai Lok, Gao, Bin and Anyanwu, Tobenna (2022) Artificial Intelligence for Emerging Technology in Surgery: Systematic Review and Validation. IEEE Reviews in Biomedical Engineering. ISSN 1937-3333 (In Press)

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Official URL: https://doi.org/10.1109/RBME.2022.3183852

Abstract

Surgery is a high-risk procedure of therapy and is associated to post trauma complications of longer hospital stay, estimated blood loss and long duration of surgeries. Reports have suggested that over 2.5% patients die during and post operation. This paper is aimed at systematic review of previous research on artificial intelligence (AI) in surgery, analyzing their results with suitable software to validate their research by obtaining same or contrary results. Six published research articles have been reviewed across three continents. These articles have been re-validated using software including SPSS and MedCalc to obtain the statistical features such as the mean, standard deviation, significant level, and standard error. From the significant values, the experiments are then classified according to the null (p<0.05) or alternative (p>0.05) hypotheses. The results obtained from the analysis have suggested significant difference in operating time, docking time, staging time, and estimated blood loss but show no significant difference in length of hospital stay, recovery time and lymph nodes harvested between robotic assisted surgery using AI and normal conventional surgery. From the evaluations, this research suggests that AI-assisted surgery improves over the conventional surgery as safer and more efficient system of surgery with minimal or no complications.

Item Type: Article
Uncontrolled Keywords: Surgery, artificial intelligence, robotic-assisted surgery, systematic review, docking time
Subjects: B800 Medical Technology
G700 Artificial Intelligence
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 06 Sep 2022 13:51
Last Modified: 06 Sep 2022 14:00
URI: https://nrl.northumbria.ac.uk/id/eprint/50044

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