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Accès ouvert déclaré 2025 article

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

19Citations signalées — pas une note de qualité
62Institutions déclarées
32Pays d’affiliation déclarés

Résumé fourni par la source

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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Contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery
Date Crossref
18/03/2025
Éditeur
Springer Science and Business Media LLC
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.

Institutions déclarées

Iran University of Medical SciencesPontificia Universidad Católica de ChileWhittington HospitalUniversity College LondonMcMaster UniversityCleveland ClinicNational University of SingaporeUniversity of PennsylvaniaMassachusetts General HospitalBergman ClinicsRoyal HospitalKing Saud Medical CityKing Saud UniversityFachverband Gebäude-Klima (Germany)Universidad México Americana del NorteHospital Alemão Oswaldo CruzSapienza University of RomeAZ Sint-JanMakassed General HospitalMedical University of ViennaUniversidad Autónoma de NayaritLouisiana State University Health Sciences Center New OrleansAin Shams UniversityMayo ClinicJordan HospitalCentre Hospitalier Interrégional Edith CavellYotsuya Medical CubeAl-Sabah HospitalChelsea and Westminster HospitalFlinders UniversityMetropolitan HospitalSir H.N. Reliance Foundation Hospital and Research CentreHospital Clínico de la Universidad de ChileSouth Tyneside and Sunderland NHS Foundation TrustUniversity of Illinois ChicagoFederico II University HospitalBrigham and Women's HospitalHarvard UniversityUniversidad de Buenos AiresCHI Memorial Medical GroupMemorial HospitalUniversidad de TijuanaSt. Franziskus HospitalHospital Juárez de MéxicoNewark Beth Israel Medical CenterUniversity of TurkuTurku University HospitalÖrebro UniversityHeidelberg UniversityUniversity Hospital HeidelbergUniversity Medical Centre MannheimHôpital Riviera-ChablaisCleveland Clinic FloridaIstanbul University-CerrahpaşaIstanbul UniversityVall d'Hebron Hospital UniversitariMisurata UniversityFirst Affiliated Hospital of Jinan UniversityHospital do DesterroUniversidad Complutense de MadridHospital Clínico San CarlosUniversity at Buffalo, State University of New York

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Sujets associés

Radiomics and Machine Learning in Medical ImagingBariatric Surgery and OutcomesArtificial Intelligence in Healthcare and Education

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