Kermansaravi Mohammad, Chiappetta Sonja, Shahabi Shahmiri Shahab, Varas Julian, Parmar Chetan, Lee Yung, Dang Jerry T, Shabbir Asim, Hashimoto Daniel, Davarpanah Jazi Amir Hossein, Meireles Ozanan R, Aarts Edo, Almomani Hazem, Alqahtani Aayad, Aminian Ali, Behrens Estuardo, Birk Dieter, Cantu Felipe J, Cohen Ricardo V, De Luca Maurizio, Di Lorenzo Nicola, Dillemans Bruno, ElFawal Mohamad Hayssam, Felsenreich Daniel Moritz, Gagner Michel, Galvan Hector Gabriel, Galvani Carlos, Gawdat Khaled, Ghanem Omar M, Haddad Ashraf, Himpens Jaques, Kasama Kazunori, Kassir Radwan, Khoursheed Mousa, Khwaja Haris, Kow Lilian, Lainas Panagiotis, Lakdawala Muffazal, Tello Rafael Luengas, Mahawar Kamal, Marchesini Caetano, Masrur Mario A, Meza Claudia, Musella Mario, Nimeri Abdelrahman, Noel Patrick, Palermo Mariano, Pazouki Abdolreza, Ponce Jaime, Prager Gerhard, Quiróz-Guadarrama César David, Rheinwalt Karl P, Rodriguez Jose G, Saber Alan A, Salminen Paulina, Shikora Scott A, Stenberg Erik, Stier Christine K, Suter Michel, Szomstein Samuel, Taskin Halit Eren, Vilallonga Ramon, Wafa Ala, Yang Wah, Zorron Ricardo, Torres Antonio, Kroh Matthew, Zundel Natan
Department of Surgery, Minimally Invasive Surgery Research Center, Division of Minimally Invasive and Bariatric Surgery, Hazrat-E Fatemeh Hospital, Iran University of Medical Sciences, Tehran, Iran.
Ospedale Evangelico Betania, Naples, Italy.
Sci Rep. 2025 Mar 18;15(1):9312. doi: 10.1038/s41598-025-94335-0.
Artificial intelligence (AI) is transforming the landscape of medicine, including surgical science and practice. The evolution of AI from rule-based systems to advanced machine learning and deep learning algorithms has opened new avenues for its application in metabolic and bariatric surgery (MBS). AI has the potential to enhance various aspects of MBS, including education and training, decision-making, procedure planning, cost and time efficiency, optimization of surgical techniques, outcome and complication prediction, patient education, and access to care. However, concerns persist regarding the reliability of AI-generated decisions and associated ethical considerations. This study aims to establish a consensus on the role of AI in MBS using a modified Delphi method. A panel of 68 leading metabolic and bariatric surgeons from 35 countries participated in this consensus-building process, providing expert insights into the integration of AI in MBS. Of the 28 statements evaluated, a consensus of at least 70% was achieved for all, with 25 statements reaching consensus in the first round and the remaining three in the second round. Experts agreed that AI has the potential to enhance the evaluation of surgical skills in MBS by providing objective, detailed assessments, enabling personalized feedback, and accelerating the learning curve. Most experts also recognized AI's role in identifying qualified candidates for MBS referrals, helping patient and procedure selection, and addressing specific clinical questions. However, concerns were raised about the potential overreliance on AI-generated recommendations. The consensus emphasized the need for ethical guidelines governing AI use and the inclusion of AI's role in decision-making within the patient consent process. Furthermore, the results suggest that AI education should become an essential component of future surgical training. Advancements in AI-driven robotics and AI-integrated genomic applications were also identified as promising developments that could significantly shape the future of MBS.
人工智能（AI）正在改变医学领域，包括外科科学与实践。人工智能从基于规则的系统发展到先进的机器学习和深度学习算法，为其在代谢与减重手术（MBS）中的应用开辟了新途径。人工智能有潜力提升MBS的各个方面，包括教育与培训、决策制定、手术规划、成本和时间效率、手术技术优化、结果与并发症预测、患者教育以及医疗服务可及性。然而，对于人工智能生成决策的可靠性以及相关伦理考量的担忧依然存在。本研究旨在使用改良德尔菲法就人工智能在MBS中的作用达成共识。来自35个国家的68位顶尖代谢与减重外科医生组成的专家小组参与了这一共识构建过程，就人工智能在MBS中的整合提供了专业见解。在评估的28条陈述中，所有陈述均达成了至少70%的共识，其中25条陈述在第一轮达成共识，其余三条在第二轮达成共识。专家们一致认为，人工智能有潜力通过提供客观、详细的评估、实现个性化反馈以及加速学习曲线来提升MBS中手术技能的评估。大多数专家还认识到人工智能在确定MBS转诊合格候选人、帮助患者和手术选择以及解决特定临床问题方面的作用。然而，有人对可能过度依赖人工智能生成的建议提出了担忧。共识强调了制定人工智能使用伦理准则的必要性，以及在患者同意过程中纳入人工智能在决策中的作用。此外，结果表明人工智能教育应成为未来外科培训的重要组成部分。人工智能驱动的机器人技术和人工智能整合的基因组应用方面的进展也被确定为有前景的发展方向，可能会显著塑造MBS的未来。
