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). 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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Journal
Scientific Reports
Published
2025-03-18
DOI
https://doi.org/10.1038/s41598-025-94335-0
Citations
7
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
9.52
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International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery

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International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery

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article en
7 citations

Abstract

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.

Scientific ReportsVol. 15(1)
Ain Shams University (EG), Universidad Complutense de Madrid (ES), Newark Beth Israel Medical Center (US), Brigham and Women's Hospital (US), Cleveland Clinic (US), Mayo Clinic (US), Harvard University (US), Misurata University (LY), University of Turku (FI), Iran University of Medical Sciences (IR), Pontificia Universidad Católica de Chile (CL), National University of Singapore (SG), Flinders University (AU), Heidelberg University (DE), Universidad de Buenos Aires (AR), Örebro University (SE), Sir H.N. Reliance Foundation Hospital and Research Centre (IN), Jordan Hospital (JO), Universidad Autónoma de Nayarit (MX), King Saud Medical City (SA), Turku University Hospital (FI), University Hospital Heidelberg (DE), King Saud University (SA), Whittington Hospital (GB), Hospital Clínico San Carlos (ES), University of Illinois Chicago (US), Massachusetts General Hospital (US), CHI Memorial Medical Group (US), Hospital Alemão Oswaldo Cruz (BR), Fachverband Gebäude-Klima (Germany) (DE), Bergman Clinics (NL), Cleveland Clinic Florida (US), Istanbul University-Cerrahpaşa (TR), Federico II University Hospital (IT), AZ Sint-Jan (BE), Vall d'Hebron Hospital Universitari (ES), Al-Sabah Hospital (KW), Hospital Clínico de la Universidad de Chile (CL), Royal Hospital (OM), Universidad de Tijuana (MX), Universidad México Americana del Norte (MX), South Tyneside and Sunderland NHS Foundation Trust (GB), Chelsea and Westminster Hospital (GB), Centre Hospitalier Interrégional Edith Cavell (BE), Hôpital Riviera-Chablais (CH), St. Franziskus Hospital (DE), Metropolitan Hospital (GR), Yotsuya Medical Cube (JP), Hospital Juárez de México (MX), University Medical Centre Mannheim (DE), Makassed General Hospital (LB), Memorial Hospital (US), First Affiliated Hospital of Jinan University (CN), Hospital do Desterro (PT), University College London (GB), University at Buffalo, State University of New York (US), Istanbul University (TR), Louisiana State University Health Sciences Center New Orleans (US), Medical University of Vienna (AT), University of Pennsylvania (US), Sapienza University of Rome (IT), McMaster University (CA)
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Radiomics and Machine Learning in Medical Imaging
9.52
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