A1 Refereed original research article in a scientific journal
International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery
Authors: 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
Publisher: NATURE PORTFOLIO
Publishing place: BERLIN
Publication year: 2025
Journal: Scientific Reports
Journal name in source: SCIENTIFIC REPORTS
Journal acronym: SCI REP-UK
Article number: 9312
Volume: 15
Issue: 1
Number of pages: 11
ISSN: 2045-2322
DOI: https://doi.org/10.1038/s41598-025-94335-0
Web address : https://www.nature.com/articles/s41598-025-94335-0
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/491735582
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.
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