Abstract:Background : The integration of robotic systems and virtual reality technologies in gastrointestinal surgery has transformed the field, offering improved precision, reduced morbidity, and enhanced patient outcomes. However, several key concerns remain unaddressed, including the high upfront costs and maintenance requirements of these advanced technologies, which may limit their adoption in low-resource settings and exacerbate existing healthcare disparities.Methods : This review provides a comprehensive overview of the current state of laparoscopic and robot-assisted surgeries in gastrointestinal surgery, highlighting the benefits and limitations of these advanced technologies.Results : The review explores the potential future of minimally invasive surgery, including the need for cost-effectiveness analyses, strategies for increasing accessibility in low-resource settings, and targeted training programs to support surgeons in transitioning to these advanced systems.Conclusions : The review concludes with specific recommendations for clinical practice and further research, emphasizing the importance of a balanced view of the advancements in laparoscopic and robot-assisted surgeries.Novel Aspects :Emphasis on accessibility : Unlike previous reviews, this paper highlights the importance of addressing the high upfront costs and maintenance requirements of advanced technologies, and explores strategies for increasing accessibility in low-resource settings.Comprehensive overview of future directions : This review provides a detailed examination of the potential future of minimally invasive surgery, including the need for cost-effectiveness analyses and targeted training programs, which is a novel contribution to the field.Balanced view of advancements : The paper’s emphasis on presenting a balanced view of the benefits and limitations of laparoscopic and robot-assisted surgeries sets it apart from previous reviews, which may have focused primarily on the benefits of these technologies.

Early disease detection is crucial for effective treatment and improved patient outcomes. Traditional methods of disease detection rely on manual analysis of medical data, which can be time-consuming and prone to errors. The advent of Artificial Intelligence (AI) has transformed the field of disease detection, enabling rapid and accurate diagnosis. This review paper compares traditional and AI-driven methods of disease detection, focusing on Parkinson’s, cancer, metabolic, and genetic diseases. We discuss the advantages and limitations of AI-based approaches, including machine learning and deep learning, and their potential to revolutionize disease detection.