Introduction: One of the medical specializations that produces extremely huge datasets thatartificial intelligence (AI) can process in-depth and thoroughly is surgery. The phrase artificialintelligence (AI) refers to a collection of computer technologies that allow algorithms to analyze,comprehend, forecast, or act independently on clinical data. A range of statistical methods andalgorithms are used in
AI models that aim to replicate human cognitive processes, enablingmachines to take in information from and react to their surroundings. This study was conductedwith the aim of a systematic review regarding the applications of
AI in surgery.Methods: A systematic review on review studies was conducted by searching the keywords (AIand surgery), in the title, abstract in Embase, Web of Science, Scopus, PubMed scientificdatabases on February ۱, ۲۰۲۴. In this study, the Guidelines and Preferred Items of SystematicReview Studies (PRISMA) were followed. Review studies that investigated the application, roleand evaluation of
AI in
surgery and whose full text was available in English were considered asinclusion criteria without time limits.Results: Finally, ۸ studies were included in this review. In our study, the reviewed surgeriesincluded motion analysis, urology, obstetrics, gynecology, tissue retraction, cardiac, cataract,pediatric, orthopedic, plastic, and reconstructive surgery. The quality of the reviewed studies waspoor. The tiny size of the datasets limits the conclusions. Less than half of the models could beunderstood, and the great majority were not validated. Only a small number of published AIalgorithms were impartial, interpretable, and externally evaluated. The short datasets, lack ofexternal validation, use of algorithms without training information, and lack of use of non-specialist language to explain them to users (surgeons) are only a few of the constraints on AIresearch that the current review finds. According to the statistics, deep learning still has difficultiesin recognizing surgical expertise and complications.Conclusion: This review has highlighted AI's enormous promise in the field of surgery, with theexpectation that it will enhance or completely change every facet of surgical patient care. Databias and drift, patient safety, ethics, governance, and cybersecurity are some of the main obstaclespreventing its widespread adoption. Every surgical specialty is looking into the potential of
AI tomaximize clinical efficiency, but a large portion of the research to date is still in the preclinicalstage. Collaboration will be necessary for the integration of
AI into routine clinical practice in thefuture. To evaluate the difficulties and guarantee accuracy and safety for use in clinical practice,further robust research must be conducted.
AI can be used for diagnosis and screening in order toenable prompt treatment.
