Recommendations to enhance Efficiency of Surgical Robots

1 Recommendations to Enhance the Efficiency of Surgical Robots Surgical robots have emerged as groundbreaking tools capable of executing various medical procedures. Davies (2015) highlights their versatility and applicability in various surgical domains, including minimally invasive procedures, such as cardiac interventions, neurosurgical operations, and orthopedic surgeries. The technology employs a combination of robotic arms, advanced cameras, and specialized instruments, functioning in synergy with the surgeon's expertise to undertake intricate procedures. A significant advantage of surgical robots is their remarkable precision and accuracy. According to Mayor et al. (2022), the robotic systems execute movements with submillimeter precision, a level of finesse that surpasses human capabilities. Nevertheless, the application and the usability of surgical robots are challenged by multiple factors, including the need for trained skills to monitor and operate the robots, a unique programming language that may introduce bias and language barrier in the healthcare practice, and the high costs of acquisition and maintenance of surgical robots further widening the equity gap between the economically privileged and people who can barely pay for the services. The presenting problems limiting the efficiency of surgical robots can be solved by adopting standardized surgical robot platforms and training, implementing user-friendly interfaces and control systems, and utilizing ethical and bias-mitigating programming languages.

Significant Challenges in adopting and using Surgical Robots and Who is the most significant Harm.

The high costs of surgical robots are the most significant challenge limiting the accessibility of related healthcare services. Whereas the costs of surgical robots are not static and vary depending on factors such as brand, specific surgical applications, and level of support

2 needed, the technology remains one of the most costly aspects of modern healthcare. For instance, the Da Vinci Surgical System's initial costs range from $1.5 million to $2.5 million.

This is in addition to ongoing maintenance, accessory, and support costs. The high cost of surgical robots limits their accessibility and affordability to the patient populations, compromising the initial motive of enhancing the quality and efficiency of care. Healthcare providers are also adversely affected by the high care costs due to the significant start-up capital.

Yet, the technological framework lays a substantial basis for competitiveness in healthcare technology.

The need for trained professionals to support the surgical robust is also a potential challenge likely to affect patients negatively. Ideally, surgical robots are dynamic and keep evolving, implying the need for ongoing learning and training. Yet, the training is also capital and time-intensive, reducing organizational productive time. Medical service providers must strike a balance between skills and profits, as inadequate skills are likely to bring about medical errors, which would have adverse implications for both the patient and the provider.

The challenge of programming language vulnerability to bias is likely to compromise the efficiency of surgical robots. Apparently, surgical robots are controlled and automated using unique programming languages. Lawrie et al. (2022) emphasize that the choice of a programming language plays a crucial role in enhancing a surgical robot's safety, functionality, and ethical basis. For instance, low-level language enables precise movements and manipulations of robotic arms, effective user interface, and effective data processing. Nevertheless, the potential for bias in a programming language is an issue that would compromise the efficiency of robots. For example, supposing the algorithms used in the robots are trained based on nonrepresentative data, then disparities in the treatment processes are likely. The biased algorithm is

3 also likely to increase differences in patient care and lead to decisions that are not representative of the healthcare population. Other potential problems with biased algorithms include challenges determining accountability and transparency and changes in data interoperability. Integrating surgical robots with other medical technologies would make it less challenging.

Recommendations to Solve the Problems As aforementioned, the identified problems can be solved using an interplay of factors, including standardization of surgical robot platforms to encourage the establishment of opensource standard robots that can easily be integrated into the healthcare industrial context. The challenges due to costs and training can also be solved by promoting subsidized training programs for robotic surgery. The programs should be standardized to be widely affordable and accessible to interested healthcare professionals. The subsidies should also be on surgical robots' costs to make them relatively easier and affordable.

Implementing a user-friendly control system to reduce the need for specialist training.

The control systems would enhance the surgical robots' simplicity and provide real-time feedback, laying the basis for further technological development. Implementing augmented reality ( AR) and Virtual Reality (VR) would also help solve the challenges associated with specialists' skills to operate surgical robots (Seetohul et al., 2023). The technologies provide medical practitioners with enhanced and interactive learning experiences, facilitating the transition from theoretical learning to practical experiences.

The potential problem of bias from the programming language can be mitigated by utilizing representative programming language with minimal risk of bias and unethical practices such as promoting disparities. Multiple strategies can be implemented to curtail the issue,

4 including implementing diverse and representative data during the development and training of the robots, regularly updating the robots to ensure they match the patient diversity, and regular audits and assessments to identify any incidence of bias and unethical implications.

Conclusion While it is true that surgical robots have revolutionized the healthcare industry by enhancing precision and accuracy, issues such as the high costs of care, the need for specialists' support, and potential bias during programming and training of the robots limit their efficiency in enhancing the quality of care and overall sustainability. This explains the need for human oversight during robot-assisted procedures and interdisciplinary collaboration during the robots' design, programming, and testing. Of more importance is the continuous improvement of the technology to integrate changing demographic trends, industrial trends, and consumer needs and preferences. Undoubtedly, mitigating potential bias and fostering equity when using surgical robots require an ongoing dedication and commitment to fairness, inclusivity, quality, and sustainable healthcare.

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References Davies, B. (2015). Robotic Surgery – A Personal View of the Past, Present and Future.

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https://doi.org/10.5772/60118 Lawrie, L., Gillies, K., Duncan, E., Davies, L., Beard, D., & Campbell, M. K. (2022). Barriers and enablers to the effective implementation of robotic assisted surgery. PLOS ONE, 17(8), e0273696. https://doi.org/10.1371/journal.pone.0273696 Mayor, N., Coppola, A. S., & Challacombe, B. (2022). Past, present, and future of surgical robotics. Trends in Urology & Men’s Health, 13(1), 7–10. https://doi.org/10.1002/tre.834 Seetohul, J., Shafiee, M., & Sirlantzis, K. (2023). Augmented Reality (AR) for Surgical Robotic and Autonomous Systems: State of the Art, Challenges, and Solutions. Sensors, 23(13), 6202. https://doi.org/10.3390/s23136202