Evolving policies for surgical AI and robotics

Co-Authors: Nicolas Zucchini¹, Christian Macias², Aman Goyal³, Abhirami Babu⁴, Adel Abou-Mrad⁵, Adolfo Perez-Bonet⁶

¹ The Robotic Global Surgical Society (TROGSS)
² TROGSS; Health Science Department, Hillsborough College, Tampa; School of Medicine, Universidad Catolica de Santiago de Guayaquil, Ecuador 
³ TROGSS; Department of General Surgery, Mahatma Gandhi Medical College and Research Institute, Sri Balaji Vidhyapeeth, India; Adesh Institute of Medical Sciences and Research, Bathinda, India
⁴ TROGSS; School of Medicine, Our Lady of Fatima University, Manila, Philippines 
⁵ TROGSS; Department of Surgery, Centre Hospitalier Universitaire d’Orléans, France
⁶ TROGSS; Department of Surgery, Gloria Patricia Pinzon Clinic, Colombia

As artificial intelligence and robotics reshape surgical practice, policy and regulatory frameworks must keep pace to address issues such as accountability, safety, and equity. 

DOI: https://doi.org/10.25453/plabs.32788989

Published on June 25th, 2026

Introduction 

Artificial intelligence (AI) and robotics are actively shaping surgical practice across the entire perioperative continuum, as Granados et al. describe in their Frontiers in Science lead article (1). From diagnostics to intraoperative navigation and postoperative monitoring, these tools reflect the broader transition toward data-driven, high-performance medicine (2). However, governance structures have not kept pace, leaving a widening gap between innovation and oversight (3). 

This disconnect has direct implications for accountability, safety, and equity. AI systems increasingly influence clinical decisions yet remain insufficiently regulated in terms of validation, transparency, and long-term monitoring (4). Robotic platforms are simultaneously redefining surgical workflows and capabilities (5). Without deliberate policy direction, these advancements risk reinforcing global disparities in access to safe surgical care, as highlighted by Global Surgery 2030 (6). The evolution of surgical teams must therefore be understood not only as a technological shift but as a policy priority requiring coordinated governance. 

Evolution of the surgical team 

The traditional surgical team, anchored around the authority of the operating surgeon, is evolving into a complex, interdisciplinary system. AI and robotics introduce new forms of collaboration extending beyond clinicians to include engineers, data scientists, and industry stakeholders (1, 5). Surgeons must now interpret algorithmic outputs, supervise semi-autonomous systems, and integrate machine-derived insights into clinical decision-making. 

This shift challenges existing frameworks of responsibility. While AI contributes meaningfully to decisions, accountability remains concentrated on clinicians, creating a mismatch between practice and legal structures (8). Surgical expertise is expanding to include competencies in data literacy, algorithmic interpretation, and bias recognition (4). From a policy perspective, surgical teams should be redefined as adaptive systems requiring clear role delineation, communication pathways, and oversight mechanisms. 

Algorithmic bias and equity 

AI systems have demonstrated the potential to reproduce and amplify existing healthcare disparities. Widely used algorithms have underestimated risk in Black patients compared with White patients with similar clinical profiles (8). In surgical contexts, predictive models may exhibit reduced accuracy across diverse populations when trained on limited datasets (9). These biases translate directly into inequitable care, influencing access to surgery, perioperative prioritization, and clinical outcomes. 

Bias often stems from dataset limitations, including lack of demographic diversity, inconsistent data collection, and institutional variability (3). The opaque nature of many machine learning models further complicates detection and mitigation (10). AI systems developed in high-resource settings may not generalize effectively to low- and middle-income countries, risking further disparities in global surgical care (1, 5). Ensuring equity requires enforceable standards for dataset diversity, subgroup performance reporting, and continuous auditing. 

Regulatory and governance challenges 

Current regulatory models, designed for static technologies, are insufficient for adaptive AI systems that evolve over time (11). Continuous-learning algorithms challenge traditional approval processes, necessitating a shift toward lifecycle-based regulation. While initiatives such as the United States Food and Drug Administration’ Software as a Medical Device framework and the European Union AI Act represent progress, significant gaps remain in addressing real-time clinical use and intraoperative unpredictability (12, 13). A central principle must be the preservation of human oversight; AI should function strictly as a decision-support tool, not an autonomous authority (7). Robust post-market surveillance systems are essential to monitor algorithm performance, detect drift, and ensure patient safety over time. 

Surgical training and workforce 

AI and robotics demand a transformation in surgical education. Surgeons must develop foundational AI literacy, including the ability to interpret algorithmic outputs and understand their limitations (4). Without this, there is risk of over-reliance on technology and erosion of clinical judgment. While simulation platforms can improve specific technical metrics, they may not fully replicate real-world surgical complexity, potentially leading to imbalanced skill development (14). Policies should promote hybrid training models integrating AI-driven feedback with human mentorship. Robotic surgery should be viewed as a teaching tool rather than a threat to traditional surgical education (15). 

Policy recommendations: a summary 

The integration of AI and robotics into surgery requires a coordinated policy architecture addressing accountability, transparency, equity, adaptability, and global collaboration as interconnected priorities.  

Accountability frameworks must evolve beyond surgeon-centric models. Responsibility should be distributed across clinicians, institutions, and developers, reflecting the shared nature of decision-making in AI-assisted care. Regulatory requirements should mandate transparency in algorithm design, including disclosure of training data sources, validation processes, and known limitations (7). 

Patient-centered transparency must be strengthened. Informed consent processes should explicitly acknowledge the role of AI in surgical care, providing patients with clear explanations of how these systems contribute to decision-making (4). Institutional-level transparency, such as public disclosure of AI systems in use, should be encouraged. 

Equity must be embedded as a regulatory requirement. Policies should enforce minimum standards for dataset diversity and require performance reporting across demographic subgroups. AI systems should undergo rigorous external validation in the populations where they will be deployed, particularly in low- and middle-income settings (6, 8). 

Adaptive regulatory models must replace static approval processes. Lifecycle-based governance should include continuous monitoring, predefined update protocols, and mechanisms for rapid intervention when safety concerns arise. Regulatory sandboxes could provide practical pathways for evaluating AI systems before widespread implementation (12, 13). 

Data governance frameworks must address the unique challenges of surgical data. Policies should ensure patient privacy while enabling responsible data sharing for AI development. Emerging approaches such as federated learning should be supported, allowing collaboration without compromising data sovereignty (16). 

Education reform is critical. AI literacy should be integrated into surgical training as a core competency, complemented by continued emphasis on human judgment and technical skill (4). Training programs should maintain balanced assessment models combining expert evaluation with algorithmic metrics. 

Finally, global collaboration is essential. International coordination is needed to harmonize regulatory standards, facilitate data sharing, and ensure equitable access to AI technologies. Without such cooperation, there is risk of creating a two-tiered system where advanced technologies are concentrated in high-resource settings (6). 

Conclusion 

AI and robotics are transforming surgery at an unprecedented pace, reshaping how teams function and decisions are made. To ensure these technologies improve care rather than complicate it, policy must evolve just as rapidly. Clear accountability, equitable implementation, adaptive regulation, and modernized education are essential. If guided thoughtfully, AI can enhance surgical precision and expand access globally. If not, it risks deepening disparities and diffusing responsibility. The direction taken now will determine whether this transformation becomes a true advancement in surgical care. 

Copyright statement 

Copyright: © 2026 [author(s)]. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in Frontiers Policy Labs is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.      

Generative AI statement 

The authors declared that generative AI was not used in the creation of this manuscript.  

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