M. Farhan1, T. Patel2, H.H. Nezhad1, J.D. John3, A. Naim4 1Ajman University, College Of Medicine, Ajman, AJMAN, United Arab Emirates 2Trinity Medical Sciences University, School Of Medicine, Kingstown, KINGSTOWN, Saint Vincent and the Grenadines 3Malla Reddy Institute of Medical Science, Hyderabad, TELANGANA, India 4Fenerbahce University, Faculty Of Health Sciences, Istanbul, ISTANBUL, Turkey
Introduction:
The implementation of robots, artificial intelligence (AI), and machine learning (ML) into neurosurgery shows potential for enhancing accuracy, minimizing human mistakes, and improving patient results. However, these technologies attempt to fill significant areas for improvement in present neurosurgical methods, such as human precision, consistency, and the ability to handle massive volumes of data for real-time decision-making.
Methods:
A systematic review was conducted in accordance with the PRISMA guidelines. The databases searched include PubMed/MEDLINE, Web of Science, Scopus, and The Cochrane Library. The inclusion criteria focused on research that evaluated the clinical implementation of robots, artificial intelligence (AI), and machine learning (ML) in neurosurgery. The Cochrane Risk of Bias Tool was utilized for quality evaluation, and data extraction was conducted according to study category, population, intervention, and outcomes.
Results:
The review identified 45 studies, including 20 randomized controlled trials, 15 observational studies, and 10 cohort studies. The key findings demonstrated that robotics enhanced surgical precision and decreased intraoperative errors, while AI and ML improved preoperative planning, intraoperative decision-making, and postoperative outcomes. However, challenges such as high costs, integration into existing surgical workflows, and the need for extensive training were highlighted.
Conclusion:
Robotics, artificial intelligence (AI), and machine learning (ML) technologies are being used to improve neurosurgical processes by increasing accuracy, ensuring consistency, and making data-driven decisions. Although there have been promising results, further investigation is required to improve the efficiency of these technologies for extensive utilization in clinical settings. This research should focus on overcoming the obstacles related to expenses, training, and integration.