The robotic PIANO: a critical narrative review of surgeon-controlled operative polyphony, human factors, and robotic training
Abstract
Abstract Robotic surgery can concentrate control of several technical functions at the console, but such concentration does not itself confer clinical independence, reduce dependence on the perioperative team, or improve patient outcomes. We define surgeon-controlled operative polyphony as the phase-bounded coordination of multiple partially independent technical control streams by one console operator within a distributed perioperative system. This critical narrative review and conceptual synthesis asks which technical functions can be concentrated coherently, where human-factors and team dependencies set limits, and how evaluation, training, and governance should respond. The piano is used as a question-generating model, not as evidence of clinical benefit. PubMed/MEDLINE was searched from inception through 18 August 2026; targeted engineering, educational, neuroscientific, human-factors, and musicological searches and backward citation tracking supplemented retrieval. To analyze this control configuration, we propose the PIANO framework: Phase-specific control, Interface fidelity, Agency allocation, Neuromotor integration, and Orchestrated teamwork. Its scientific novelty lies in linking this control architecture to the PIANO framework: Phase-specific control, Interface fidelity, Agency allocation, Neuromotor integration, and Orchestrated teamwork. PIANO is an analytic and hypothesis-generating framework, not a score or credentialing standard. Concentrated control may shorten selected action loops, but any benefit depends on feedback, workload, shared situation awareness, explicit authority, and rescue capacity. The Competency-Based Assessment of Robotic Surgery Skills (CARS) curriculum provides initial validation evidence for domain-specific assessment of performer competence; repeated cases and use-specific evidence remain necessary for high-stakes decisions. Artificial intelligence (AI) may support phase recognition, feedback, assessment, and personalised training within a human-in-the-loop system. One surgeon may coordinate many operative voices; safe surgery remains a collective, patient-centered performance.
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Authors: Luigi Marano, Mirko Barone, Mehmet Mahir Özmen, Bojan Jovanović, Francesco Paolo Prete, Silvia Malerba, Edmundo Inga-Zapata, Panagiotis Laïnas, Elena Ruiz‐Úcar, Miguel Angel Cáceres Yap, Vikas Jain, Beniamino Pascotto, Juan Santiago Azagra, Salaam Sadi, Руслан Дука, Robert Molchanov, Adolfo Perez-Bonet, Nicolas Zucchini, Luis Osvaldo Suárez Carreón, Jarosław Skokowski, Adebusola Adenike Owokole, Archil Aladashvili, Miljana Vladimirov, Mahmoud Al-Balas, Cesar Santiago, Sharona Ross, Sharique Nazir, Yogesh Vashist, Adel Abou-Mrad, Mario Testini, Ludovico Docimo, David Albala, Rodolfo J. Oviedo
Institutions: University of Bari Aldo Moro, Hashemite University, Virginia Commonwealth University, University of Chieti-Pescara, University of Houston, Hacettepe University, Ospedale SS. Annunziata, Qatar University, European University Cyprus, University of Campania "Luigi Vanvitelli", Université Paris-Saclay, University of Nis, New York City Health and Hospitals Corporation, Tbilisi State Medical University, Universidad Rey Juan Carlos, University Hospital Limerick, Liv Hospital, Centre Hospitalier de Luxembourg, Centre hospitalier universitaire d'Orléans, Klinikum Lippe, Hospital Universitario de Fuenlabrada, Mexican Social Security Institute, HCG Cancer Centre, Universidad de San Martín de Porres, AdventHealth Tampa, Metropolitan Hospital, Sam Houston State University, Hôpital Antoine-Béclère, Prywatna Wyższa Szkoła Nauk Społecznych, Komputerowych i Medycznych, Hospital Punta Pacifica, Sana Kliniken AG, Crouse Hospital