International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery
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)....
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
18 March 2025
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| In: |
Scientific reports
Year: 2025, Jahrgang: 15, Pages: 1-11 |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-025-94335-0 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41598-025-94335-0 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41598-025-94335-0 
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| Verfasserangaben: | Mohammad Kermansaravi, Sonja Chiappetta, Shahab Shahabi Shahmiri, Julian Varas, Chetan Parmar, Yung Lee, Jerry T. Dang, Asim Shabbir, Daniel Hashimoto, Amir Hossein Davarpanah Jazi, Ozanan R. Meireles, Edo Aarts, Hazem Almomani, Aayad Alqahtani, Ali Aminian, Estuardo Behrens, Dieter Birk, Felipe J. Cantu, Ricardo V. Cohen, Maurizio De Luca, Nicola Di Lorenzo, Bruno Dillemans, Mohamad Hayssam ElFawal, Daniel Moritz Felsenreich, Michel Gagner, Hector Gabriel Galvan, Carlos Galvani, Khaled Gawdat, Omar M. Ghanem, Ashraf Haddad, Jaques Himpens, Kazunori Kasama, Radwan Kassir, Mousa Khoursheed, Haris Khwaja, Lilian Kow, Panagiotis Lainas, Muffazal Lakdawala, Rafael Luengas Tello, Kamal Mahawar, Caetano Marchesini, Mario A. Masrur, Claudia Meza, Mario Musella, Abdelrahman Nimeri, Patrick Noel, Mariano Palermo, Abdolreza Pazouki, Jaime Ponce, Gerhard Prager, César David Quiróz-Guadarrama, Karl P. Rheinwalt, Jose G. Rodriguez, Alan A. Saber, Paulina Salminen, Scott A. Shikora, Erik Stenberg, Christine K. Stier, Michel Suter, Samuel Szomstein, Halit Eren Taskin, Ramon Vilallonga, Ala Wafa, Wah Yang, Ricardo Zorron, Antonio Torres, Matthew Kroh & Natan Zundel |
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| 245 | 1 | 0 | |a International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery |c Mohammad Kermansaravi, Sonja Chiappetta, Shahab Shahabi Shahmiri, Julian Varas, Chetan Parmar, Yung Lee, Jerry T. Dang, Asim Shabbir, Daniel Hashimoto, Amir Hossein Davarpanah Jazi, Ozanan R. Meireles, Edo Aarts, Hazem Almomani, Aayad Alqahtani, Ali Aminian, Estuardo Behrens, Dieter Birk, Felipe J. Cantu, Ricardo V. Cohen, Maurizio De Luca, Nicola Di Lorenzo, Bruno Dillemans, Mohamad Hayssam ElFawal, Daniel Moritz Felsenreich, Michel Gagner, Hector Gabriel Galvan, Carlos Galvani, Khaled Gawdat, Omar M. Ghanem, Ashraf Haddad, Jaques Himpens, Kazunori Kasama, Radwan Kassir, Mousa Khoursheed, Haris Khwaja, Lilian Kow, Panagiotis Lainas, Muffazal Lakdawala, Rafael Luengas Tello, Kamal Mahawar, Caetano Marchesini, Mario A. Masrur, Claudia Meza, Mario Musella, Abdelrahman Nimeri, Patrick Noel, Mariano Palermo, Abdolreza Pazouki, Jaime Ponce, Gerhard Prager, César David Quiróz-Guadarrama, Karl P. Rheinwalt, Jose G. Rodriguez, Alan A. Saber, Paulina Salminen, Scott A. Shikora, Erik Stenberg, Christine K. Stier, Michel Suter, Samuel Szomstein, Halit Eren Taskin, Ramon Vilallonga, Ala Wafa, Wah Yang, Ricardo Zorron, Antonio Torres, Matthew Kroh & Natan Zundel |
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| 520 | |a 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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