The introduction of minimally invasive surgery was a revolution compared to open surgery, as “keyhole surgery” significantly reduced postoperative pain due to smaller incisions, thus shortened recovery time and hospital stay, and allowed faster return to regular activity. The contribution of robotic assistance to improve surgical care is obvious compared to open surgery, but remains controversial when compared to laparoscopy, and largely depends on the surgeon and the procedure.
Minimally invasive surgery requires intense surgical training and special equipment for both laparoscopy and robotic surgery. The latter, in particular, involves substantial investment costs. At the same time, 5 billion people worldwide do not have timely access to safe surgery at all, and affordability remains a critical issue [1]. The lack of dedicated reimbursement for robotic surgery in many countries places an additional burden on hospitals and limits its utility to medium and high complexity procedures [2]. For frequent and less complex procedures, laparoscopic surgery is the accepted standard, can be performed by most surgeons without robotic assistance, and is less costly. The current health economic situation and the price of robotic surgery accentuate inequalities in care. Yet, the remarkable technological progress has led to ever-increasing adoption of robotic surgery over the last decades [3]. High-income countries drive progress and therefore have the technological and policymaking power to address quality of care and reduce inequalities. In this context, what role can robotic surgery and artificial intelligence play in terms of economic impact and risk reduction?
To answer this, robotic surgical systems should be seen as digital interfaces. They not only allow to precisely control instrument movements and high-resolution 3D camera views. They also enable the recording of instrument usage and motion data, the storage of surgical videos, and – more recently – the comparison of performance between different users of a system.
Surgical training is not only based on direct teaching – such as in a double-console setup in robotic surgery – but also on comparing one’s own technique and performance with that of worldwide experts in the field, e.g. via lectures and video databases including IRCAD’s freely accessible online university1. However, accessing and learning from a large amount of data sources can take years for humans in complex situations, but only a matter of days for AI. The computer program AlphaGo learned to play Go from human games and moves, and defeated champions nearly a decade ago. Later, the deep neural network of AlphaGo Zero was trained by playing against itself using a reinforcement learning algorithm, achieved superhuman performance in this challenging domain, and even played previously unknown moves [4]. AI could be utilized for any surgical case, no matter how rare, to identify similar cases among worldwide video recordings of operations and thus provide tailored support and guidance. As much as the accessibility of large datasets poses a challenge to the legal frameworks of data protection, their analysis is expected to enable faster identification of patients at risk and more effective interventions, leading to hundreds of billions of dollars in reduced healthcare spending [5,6].
Building systems to improve access to knowledge and robust data collection will accelerate worldwide access to the best surgical care. Currently, guidance through implemented imaging technology and remote connections with expert surgeons promote the dissemination of best practices in robotic surgery [7,8]. Based on a global analysis of various surgical data sources, a comparable AI assistance system could suggest ways to achieve and possibly surpass current best standards in surgery. Collaboration between academia and industry is essential to integrate trustworthy benchmarks into commercial AI solutions. Experienced surgeon scientists should therefore seize the opportunity to contribute to the development of AI assistance systems with clinical relevance. The integration of AI into robotic surgical systems can then transform the healthcare industry and provide universal access to expert knowledge and techniques.
Funding: This work was supported by French state funds managed within the “Plan Investissements d’Avenir” and by the ANR (reference ANR-10-IAHU-02).
Note :
1- www.websurg.com (Consulté le 13-06-2023).
References
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Citation
Doi : 10.25329/rq_xx_2_edito-en.