The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring
Lee, N. Robotic surgery: where are we now? Lancet 384, 1417 (2014).
Google Scholar
Kwoh, Y. S., Hou, J., Jonckheere, E. A. & Hayati, S. A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Trans. Biomed. Eng. 35, 153–160 (1988).
Google Scholar
Peters, B. S., Armijo, P. R., Krause, C., Choudhury, S. A. & Oleynikov, D. Review of emerging surgical robotic technology. Surg. Endosc. 32, 1636–1655 (2018).
Google Scholar
Maynou, L., Pearson, G., McGuire, A. & Serra-Sastre, V. The diffusion of robotic surgery: examining technology use in the English NHS. Health Policy 126, 325–336 (2022).
Google Scholar
The Lancet. Robotic surgery evaluation: 10 years too late. Lancet 388, 1026 (2016).
Christensen, C. M., Baumann, H., Ruggles, R. & Sadtler, T. M. Disruptive innovation for social change. Harv. Bus. Rev. 84, 94–101 (2006).
Google Scholar
Tan, W. S., Ta, A. & Kelly, J. D. Robotic surgery: getting the evidence right. Med J. Aust. 217, 391–393 (2022).
Google Scholar
Vasey, B. et al. Intraoperative applications of artificial intelligence in robotic surgery: a scoping review of current development stages and levels of autonomy. Ann. Surg. (2023).
Ergina, P. L., Barkun, J. S., McCulloch, P., Cook, J. A. & Altman, D. G. IDEAL framework for surgical innovation 2: observational studies in the exploration and assessment stages. BMJ 346, f3011 (2013).
Google Scholar
Hirst, A. et al. No surgical innovation without evaluation: evolution and further development of the IDEAL framework and recommendations. Ann. Surg. 269, 211–220 (2019).
Google Scholar
NIHR. REINFORCE: a real-world, in-situ, evaluation of the introduction and scale-up of robot-assisted surgical services in the NHS. ARC https://arc-nenc.nihr.ac.uk/projects/reinforce-a-real-world-in-situ-evaluation-of-the-introduction-and-scale-up-of-robot-assisted-surgical-services-in-the-nhs/
Sheetz, K. H., Claflin, J. & Dimick, J. B. Trends in the adoption of robotic surgery for common surgical procedures. JAMA Netw. Open 3, e1918911 (2020).
Google Scholar
Future of Surgery Commission Group. The Commission on the Future of Surgery. (2018).
McCulloch, P., Cook, J. A., Altman, D. G., Heneghan, C. & Diener, M. K. IDEAL framework for surgical innovation 1: the idea and development stages. BMJ 346, f3012 (2013).
Google Scholar
Cook, J. A. et al. IDEAL framework for surgical innovation 3: randomised controlled trials in the assessment stage and evaluations in the long term study stage. BMJ 346, f2820 (2013).
Google Scholar
Marcus, H. J. et al. IDEAL-D framework for device innovation: a consensus statement on the preclinical stage. Ann. Surg. (2021).
Google Scholar
UK Statutory Instruments, UK Government. The Medical Devices Regulations 2002. 2002 no. 618 (King’s Printer of Acts of Parliament).
Official Journal of the European Union. Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EEC. vol. 117 (2017).
International Electrotechnical Commission. Amendment 1 – medical electrical equipment – part 1-2: general requirements for basic safety and essential performance – collateral standard: electromagnetic disturbances – requirements and tests. IEC 60601-1-2:2014/AMD1 (2020).
International Organization for Standardization. Quality management and corresponding general aspects for products with a health purpose including medical devices. IEC 62304:2006.
International Organization for Standardization. Medical electrical equipment Part 2-77: Particular requirements for the basic safety and essential performance of robotically assisted surgical equipment. IEC 80601-82:2019.
International Organization for Standardization. Medical devices—quality management systems—requirements for regulatory purposes. ISO 13485:2016.
Foote, S. B. Managing the Medical Arms Race: Innovation and Public policy in the Medical Device Industry (Univ. California Press, 1992).
Yang, G. -Z. et al. Medical robotics—regulatory, ethical, and legal considerations for increasing levels of autonomy. Sci. Robot 2, eaam8638 (2017).
Google Scholar
Andras, I. et al. Artificial intelligence and robotics: a combination that is changing the operating room. World J. Urol. 38, 2359–2366 (2020).
Google Scholar
Bhandari, M., Zeffiro, T. & Reddiboina, M. Artificial intelligence and robotic surgery: current perspective and future directions. Curr. Opin. Urol. 30, 48–54 (2020).
Google Scholar
Panesar, S. et al. Artificial intelligence and the future of surgical robotics. Ann. Surg. 270, 223–226 (2019).
Google Scholar
Hung, A. J. et al. Development and validation of objective performance metrics for robot-assisted radical prostatectomy: a pilot study. J. Urol. 199, 296–304 (2018).
Google Scholar
Ashley, L., Armitage, G., Neary, M. & Hollingsworth, G. A practical guide to failure mode and effects analysis in health care: making the most of the team and its meetings. Jt. Comm. J. Qual. Patient Saf. 36, 351–358 (2010).
Google Scholar
Vasey, B. et al. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ 377, e070904 (2022).
Google Scholar
Schreyer, J. et al. RAS-NOTECHS: validity and reliability of a tool for measuring non-technical skills in robotic-assisted surgery settings. Surg. Endosc. 36, 1916–1926 (2022).
Google Scholar
Raison, N. et al. Development and validation of a tool for non-technical skills evaluation in robotic surgery-the ICARS system. Surg. Endosc. 31, 5403–5410 (2017).
Google Scholar
Catchpole, K. et al. Human factors integration in robotic surgery. Hum. Factors (2022).
O’Sullivan, S. et al. Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery. Int. J. Med. Robot 15, e1968 (2019).
Google Scholar
Rogers, W. A., Hutchison, K. & McNair, A. Ethical issues across the IDEAL stages of surgical innovation. Ann. Surg. 269, 229–233 (2019).
Google Scholar
Møller, L. et al. Identifying curriculum content for operating room nurses involved in robotic-assisted surgery: a Delphi study. Surg. Endosc. (2022).
Google Scholar
Lawrie, L. et al. Current issues and future considerations for the wider implementation of robotic-assisted surgery: a qualitative study. BMJ Open 12, e067427 (2022).
Google Scholar
Lawrie, L. et al. Barriers and enablers to the effective implementation of robotic assisted surgery. PLoS ONE 17, e0273696 (2022).
Google Scholar
Woudstra, K., Reuzel, R., Rovers, M. & Tummers, M. An overview of stakeholders, methods, topics, and challenges in participatory approaches used in the development of medical devices: a scoping review. Int. J. Health Policy Manag 12, 6839 (2022).
Google Scholar
van der Wilt, G. J., Gerhardus, A. & Oortwijn, W. Toward integration in the context of health technology assessment: the need for evaluative frameworks. Int. J. Technol. Assess. Health Care 33, 586–590 (2017).
Google Scholar
Health and Safety Executive. Review of Human Reliability Assessment Methods; (2009).
Boys, J. A. et al. Public perceptions on robotic surgery, hospitals with robots, and surgeons that use them. Surg. Endosc. 30, 1310–1316 (2016).
Google Scholar
Johnson, J. & Rogers, W. Innovative surgery: the ethical challenges. J. Med. Ethics 38, 9–12 (2012).
Angelos, P. Ethics and surgical innovation: challenges to the professionalism of surgeons. Int. J. Surg. 11, S2–S5 (2013).
Google Scholar
Hutchison, K., Rogers, W., Eyers, A. & Lotz, M. Getting clearer about surgical innovation: a new definition and a new tool to support responsible practice. Ann. Surg. 262, 949–954 (2015).
Google Scholar
B, H., S, D., W, O., I, C. & D, S. Harmonization of ethics in health technology assessment: a revision of the Socratic approach. Int. J. Technol. Assess. Health Care 30, 3–9 (2014).
Google Scholar
Partington, A. & Karnon, J. It’s not the model, it’s the way you use it: exploratory early health economics amid complexity comment on ‘problems and promises of health technologies: the role of early health economic modelling’. Int. J. Health Policy Manag. 10, 36–38 (2020).
Google Scholar
Rizan, C. et al. The carbon footprint of surgical operations: a systematic review. Ann. Surg. 272, 986–995 (2020).
Google Scholar
Sullivan, R. et al. Global cancer surgery: delivering safe, affordable, and timely cancer surgery. Lancet Oncol. 16, 1193–1224 (2015).
Google Scholar
Grutters, J. P. C. et al. Problems and promises of health technologies: the role of early health economic modeling. Int. J. Health Policy Manag. 8, 575–582 (2019).
Google Scholar
Bolton, W. S. et al. Disseminating technology in global surgery. Br. J. Surg. 106, e34–e43 (2019).
Google Scholar
Payne, C. J. & Yang, G. -Z. Hand-held medical robots. Ann. Biomed. Eng. 42, 1594–1605 (2014).
Google Scholar
Papadopoulou, A., Kumar, N. S., Vanhoestenberghe, A. & Francis, N. K. Environmental sustainability in robotic and laparoscopic surgery: systematic review. Br. J. Surg. 109, 921–932 (2022).
Google Scholar
Micha, J. P., Rettenmaier, M. A., Bohart, R. D. & Goldstein, B. H. Robotic-assisted surgery for the treatment of breast and cervical cancers. JSLS 26, e2022.00014 (2022).
Google Scholar
Ramirez, P. T. et al. Minimally invasive versus abdominal radical hysterectomy for cervical cancer. N. Engl. J. Med. 379, 1895–1904 (2018).
Google Scholar
Nitecki, R. et al. Survival after minimally invasive vs open radical hysterectomy for early-stage cervical cancer: a systematic review and meta-analysis. JAMA Oncol. 6, 1019–1027 (2020).
Google Scholar
US Food and Drug Administration. MAUDE – Manufacturer and User Facility Device Experience; https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfmaude/search.cfm
Hu, Y. & Strong, V. E. Robotic surgery and oncologic outcomes. JAMA Oncol. 6, 1537–1539 (2020).
Google Scholar
Yaxley, J. W. et al. Robot-assisted laparoscopic prostatectomy versus open radical retropubic prostatectomy: early outcomes from a randomised controlled phase 3 study. Lancet 388, 1057–1066 (2016).
Google Scholar
Parekh, D. J. et al. Robot-assisted radical cystectomy versus open radical cystectomy in patients with bladder cancer (RAZOR): an open-label, randomised, phase 3, non-inferiority trial. Lancet 391, 2525–2536 (2018).
Google Scholar
Feng, Q. et al. Robotic versus laparoscopic surgery for middle and low rectal cancer (REAL): short-term outcomes of a multicentre randomised controlled trial. Lancet Gastroenterol. Hepatol. 7, 991–1004 (2022).
Google Scholar
Garfjeld Roberts, P. et al. Research quality and transparency, outcome measurement and evidence for safety and effectiveness in robot-assisted surgery: systematic review. BJS Open 4, 1084–1099 (2020).
Google Scholar
Wolfenden, L. et al. Designing and undertaking randomised implementation trials: guide for researchers. BMJ 372, m3721 (2021).
Google Scholar
Khan, D. Z. et al. Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0). J. Neurosurg. 1–8 (2021).
van Amsterdam, B., Clarkson, M. J. & Stoyanov, D. Gesture recognition in robotic surgery: a review. IEEE Trans. Biomed. Eng. 68, 2021–2035 (2021).
Google Scholar
Kiyasseh, D. et al. A vision transformer for decoding surgeon activity from surgical videos. Nat. Biomed. Eng. 7, 780–796 (2023).
Google Scholar
Chen, J. et al. Use of automated performance metrics to measure surgeon performance during robotic vesicourethral anastomosis and methodical development of a training tutorial. J. Urol. 200, 895–902 (2018).
Google Scholar
Páez, A. et al. Beyond the RCT: when are randomized trials unnecessary for new therapeutic devices, and what should we do instead? Ann. Surg. 275, 324–331 (2022).
Google Scholar
Shouhed, D., Gewertz, B., Wiegmann, D. & Catchpole, K. Integrating human factors research and surgery: a review. Arch. Surg. 147, 1141–1146 (2012).
Google Scholar
Ijy, W., Lj, K. & Jc, N. A systematic review of the true benefit of robotic surgery: ergonomics. Int. J. Med. Robot. 16, e2113 (2020).
Google Scholar
Collins, J. W. & Wisz, P. Training in robotic surgery, replicating the airline industry. How far have we come? World J. Urol. 38, 1645–1651 (2020).
Google Scholar
Jayne, D. et al. Effect of robotic-assisted vs conventional laparoscopic surgery on risk of conversion to open laparotomy among patients undergoing resection for rectal cancer: the ROLARR randomized clinical trial. JAMA 318, 1569–1580 (2017).
Google Scholar
Johnson, B., Sorokin, I., Singla, N., Roehrborn, C. & Gahan, J. C. Determining the learning curve for robot-assisted simple prostatectomy in surgeons familiar with robotic surgery. J. Endourol. 32, 865–870 (2018).
Google Scholar
Pernar, L. I. M. et al. An appraisal of the learning curve in robotic general surgery. Surg. Endosc. 31, 4583–4596 (2017).
Google Scholar
Vilallonga, R. et al. The initial learning curve for robot-assisted sleeve gastrectomy: a surgeon’s experience while introducing the robotic technology in a bariatric surgery department. Minim. Invasive Surg. 2012, 347131 (2012).
Google Scholar
Wijburg, C. J. et al. Learning curve analysis for intracorporeal robot-assisted radical cystectomy: results from the EAU Robotic urology section scientific working group. Eur. Urol. Open Sci. 39, 55–61 (2022).
Google Scholar
Kirkpatrick, D. L. Techniques for evaluating training programs. Train. Dev. J. 33, 78–92 (1979).
Sridhar, A. N., Briggs, T. P., Kelly, J. D. & Nathan, S. Training in robotic surgery—an overview. Curr. Urol. Rep. 18, 58 (2017).
Google Scholar
Skjold-Ødegaard, B. & Søreide, K. Competency-based surgical training and entrusted professional activities—perfect match or a Procrustean bed? Ann. Surg. 273, e173–e175 (2021).
Google Scholar
Carpenter, B. T. & Sundaram, C. P. Training the next generation of surgeons in robotic surgery. Robot Surg. 4, 39–44 (2017).
Google Scholar
Mark Knab, L. et al. Evolution of a novel robotic training curriculum in a complex general surgical oncology fellowship. Ann. Surg. Oncol. 25, 3445–3452 (2018).
Google Scholar
Corrigan, N. et al. Exploring and adjusting for potential learning effects in ROLARR: a randomised controlled trial comparing robotic-assisted vs. standard laparoscopic surgery for rectal cancer resection. Trials 19, 339 (2018).
Google Scholar
Torrent-Sellens, J., Jiménez-Zarco, A. I. & Saigí-Rubió, F. Do people trust in robot-assisted surgery? Evidence from Europe. Int J. Environ. Res. Public Health 18, 12519 (2021).
Google Scholar
Buabbas, A. J., Aldousari, S. & Shehab, A. A. An exploratory study of public’ awareness about robotics-assisted surgery in Kuwait. BMC Med. Inform. Decis. Mak. 20, 140 (2020).
Google Scholar
Rooshenas, L. et al. The QuinteT Recruitment Intervention supported five randomized trials to recruit to target: a mixed-methods evaluation. J. Clin. Epidemiol. 106, 108–120 (2019).
Google Scholar
Salas, R. N., Maibach, E., Pencheon, D., Watts, N. & Frumkin, H. A pathway to net zero emissions for healthcare. BMJ 371, m3785 (2020).
Google Scholar
Rasheed, F. N. et al. Decarbonising healthcare in low and middle income countries: potential pathways to net zero emissions. BMJ 375, n1284 (2021).
Google Scholar
Meara, J. G. et al. Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development. Lancet 386, 569–624 (2015).
Google Scholar
Garas, G. et al. Surgical innovation in the era of global surgery: a network analysis. Ann. Surg. 271, 868–874 (2020).
Google Scholar
K, H., J, J. & D, C. Justice and surgical innovation: the case of Robotic prostatectomy. Bioethics 30, 536–546 (2016).
Google Scholar
Caro, J. J., Briggs, A. H., Siebert, U. & Kuntz, K. M. Modeling good research practices—overview: a report of the ISPOR-SMDM modeling good research practices task force-1. Value Health 15, 796–803 (2012).
Google Scholar
Ramsey, S. D. et al. Cost-effectiveness analysis alongside clinical trials II—an ISPOR good research practices task force report. Value Health 18, 161–172 (2015).
Google Scholar
Husereau, D. et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. BMC Med. 20, 23 (2022).
Google Scholar
Dreyer, N. A. Strengthening evidence-based medicine with real-world evidence. Lancet Healthy Longev. 3, e641–e642 (2022).
Google Scholar
Kramer, D. B., Xu, S. & Kesselheim, A. S. How does medical device regulation perform in the United States and the European Union? A systematic review. PLoS Med. 9, e1001276 (2012).
Google Scholar
Cooper, M. A., Ibrahim, A., Lyu, H. & Makary, M. A. Underreporting of robotic surgery complications. J. Healthc. Qual. 37, 133–138 (2015).
Google Scholar
Rajan, P. V., Kramer, D. B. & Kesselheim, A. S. Medical device postapproval safety monitoring: where does the United States stand? Circ. Cardiovasc. Qual. Outcomes 8, 124–131 (2015).
Google Scholar
Cipriani, A. et al. Generating comparative evidence on new drugs and devices after approval. Lancet 395, 998–1010 (2020).
Google Scholar
Huot, L., Decullier, E., Maes-Beny, K. & Chapuis, F. R. Medical device assessment: scientific evidence examined by the French national agency for health—a descriptive study. BMC Public Health 12, 585 (2012).
Google Scholar
Sedrakyan, A. et al. Advancing the real-world evidence for medical devices through coordinated registry networks. BMJ Surg. Inter. Health Technol. 4, e000123 (2022).
Google Scholar
Ficuciello, F., Tamburrini, G., Arezzo, A., Villani, L. & Siciliano, B. Autonomy in surgical robots and its meaningful human control: Paladyn. J. Behav. Robot. 10, 30–43 (2019).
Google Scholar
Bilbro, N. A. et al. The IDEAL reporting guidelines: a Delphi consensus statement stage specific recommendations for reporting the evaluation of surgical innovation. Ann. Surg. 273, 82–85 (2021).
Google Scholar
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
Google Scholar
van Workum, F. et al. Learning curve and associated morbidity of minimally invasive esophagectomy: a retrospective multicenter study. Ann. Surg. 269, 88–94 (2019).
Google Scholar
Oshikiri, T. et al. Short-term outcomes and one surgeon’s learning curve for thoracoscopic esophagectomy performed with the patient in the prone position. Surg. Today 47, 313–319 (2017).
Google Scholar
Zeuschner, P. et al. Three different learning curves have an independent impact on perioperative outcomes after robotic partial nephrectomy: a comparative analysis. Ann. Surg. Oncol. 28, 1254–1261 (2021).
Google Scholar
Le Morvan, P. & Stock, B. Medical learning curves and the Kantian ideal. J. Med. Ethics 31, 513–518 (2005).
Google Scholar
Dixon, F. & Keeler, B. Robotic surgery: training, competence assessment and credentialing. Bulletin 102, 302–306 (2020).
Google Scholar
Chen, R. et al. A comprehensive review of robotic surgery curriculum and training for residents, fellows, and postgraduate surgical education. Surg. Endosc. 34, 361–367 (2020).
Google Scholar
Jamjoom, A. A. B. et al. Autonomous surgical robotic systems and the liability dilemma. Front. Surg. 9, 1015367 (2022).
Google Scholar
van Wynsberghe, A. in Robotics, AI and Humanity: Science, Ethics and Policy (eds. J. von Braun et al.) 239–249 (Springer International Publishing, 2021).
Catchpole, K. et al. Safety, efficiency and learning curves in robotic surgery: a human factors analysis. Surg. Endosc. 30, 3749–3761 (2016).
Google Scholar
Poulsen, J. L., Bruun, B., Oestergaard, D. & Spanager, L. Factors affecting workflow in robot-assisted surgery: a scoping review. Surg. Endosc. 36, 8713–8725 (2022).
Google Scholar
van Dalen, A. S. H. M. et al. Analyzing and discussing human factors affecting surgical patient safety using innovative technology: creating a safer operating culture. J. Patient Saf. 18, 617–623 (2022).
Google Scholar
Messick, S. Validity. in (ed. R. L. Linn) Educational Measurement 3rd ed. pp. 13–104 (American Council on education and Macmillan, 1989).
Bai, F. et al. More work is needed on cost-utility analyses of robotic-assisted surgery. J. Evid. Based Med. 15, 77–96 (2022).
Google Scholar
Schneider, M. A. et al. Inequalities in access to minimally invasive general surgery: a comprehensive nationwide analysis across 20 years. Surg. Endosc. 35, 6227–6243 (2021).
Google Scholar
Drew, J., Christie, S. D., Rainham, D. & Rizan, C. HealthcareLCA: an open-access living database of health-care environmental impact assessments. Lancet Planet. Health 6, e1000–e1012 (2022).
Roodbeen, S. X. et al. Evolution of transanal total mesorectal excision according to the IDEAL framework. BMJ Surg. Interv. Health Technol. 1, e000004 (2019).
Google Scholar
Morrisey, Z. S. et al. Transition to robotic total knee arthroplasty with kinematic alignment is associated with a short learning curve and similar acute-period functional recoveries. Cureus 15, e38872 (2023).
Google Scholar
Kelkar, D. S., Kurlekar, U., Stevens, L., Wagholikar, G. D. & Slack, M. An early prospective clinical study to evaluate the safety and performance of the versius surgical system in robot-assisted cholecystectomy. Ann. Surg. 277, 9–17 (2023).
Google Scholar
Bell, S. W. et al. Improved accuracy of component positioning with robotic-assisted unicompartmental knee arthroplasty: data from a prospective, randomized controlled study. J. Bone Joint Surg. Am. 98, 627–635 (2016).
Google Scholar
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