The power of Artificial Intelligence (AI) combined with the surgeons’ expertise leads to breakthroughs in surgical care, bringing new hope to patients. Utilizing deep learning-based computer vision techniques in surgical procedures will enhance the healthcare industry. Laparoscopic surgery holds excellent potential for computer vision due to the abundance of real-time laparoscopic recordings captured by digital cameras containing significant unexplored information. Furthermore, with computing power resources becoming increasingly accessible and Machine Learning methods expanding across various industries, the potential for AI in healthcare is vast. There are several objectives of AI’s contribution to laparoscopic surgery; one is an image guidance system to identify anatomical structures in real-time. However, few studies are concerned with intraoperative anatomy recognition in laparoscopic surgery. This study provides a comprehensive review of the current state-of-the-art semantic segmentation techniques, which can guide surgeons during laparoscopic procedures by identifying specific anatomical structures for dissection or avoiding hazardous areas. This review aims to enhance research in AI for surgery to guide innovations towards more successful experiments that can be applied in real-world clinical settings. This AI contribution could revolutionize the field of laparoscopic surgery and improve patient outcomes.

Research issue: The study is driven by contemporary global challenges regarding the stability and efficiency of production processes, the necessity to enhance competitiveness, and ensuring workplace safety, which demands a systematic approach to monitoring and supervising adherence to labour discipline. The research is theoretical in nature. The aim/objective of the study is to analyse the specifics of state policy on supervision and control over employees’ adherence to labour discipline, the peculiarities of its practical implementation, and perspectives for improvement. Method: The study employed a logical-semantic method, analytical and documentary methods of analysis, and the method of expert assessment of labour discipline of employees and employers based on their evaluation of certain aspects of labour discipline. The research methodology included a sample size of 30 respondents, and the research instrument was expert evaluation. Data collection was conducted through surveys, and the calculation method was quantitative. Results: The article examines the impact of the main incentives and methods on ensuring labour discipline, determining their essence and forms of manifestation. It also considers the extent of application of each method in enterprise practices. It was found that economic methods are widely used and aimed at increasing employee motivation and maintaining their labour discipline. The analysis revealed that the main manifestations of employee labour discipline and managerial duties are differences in the perception of labour discipline by both parties. It was found that employers underestimate the productivity and abilities of employees, indicating potential systemic deficiencies in human resource management. Conversely, employees note that managers ignore their needs and problems. The results of the expert evaluation showed that employees rated their discipline higher than employers did. These discrepancies in evaluations could affect internal relations within the team and require managerial attention to improve interaction and cooperation. Conclusion: Based on the assessment of labour discipline, systemic deficiencies in human resource management were identified, highlighting the need for appropriate monitoring and employee motivation mechanisms. The study proposes innovative personnel management methods to ensure labour discipline in enterprises, including HR branding, team building, mentoring, and grading. It is proven that these approaches allow for the creation of a fundamentally new management system to ensure compliance with labour discipline and the development of professionalism and employee motivation.

This study examines the impact of innovation governance and policies on government funding for emerging science and technology sectors in Saudi Arabia, addressing key bureaucratic, regulatory, and cultural barriers. Using a mixed-methods approach, the research integrates qualitative insights from stakeholder interviews with quantitative survey data to provide a comprehensive under-standing of the current innovation landscape. Findings indicate a high level of policy awareness among stakeholders but reveal significant challenges in practical implementation due to bureaucratic inefficiencies and stringent regulations. Cultural barriers, such as a risk-averse mindset and traditional business practices, further impede innovation. Successful initiatives like the National Transformation Program (NTP) demonstrate the potential for well-coordinated efforts, highlighting the importance of regulatory reform and cultural shifts towards entrepreneurship. Strategic recommendations include streamlining bureaucratic processes, enhancing policy coordination, and fostering a culture of innovation through education and stakeholder engagement. This study contributes to the existing literature by offering actionable insights to enhance innovation governance, supporting Saudi Arabia’s Vision 2030 goals.