Academic Journal
The Evolution of Bariatric and Metabolic Surgery in the Artificial Intelligence Era: A Comprehensive Systematic Review of Current Applications and Clinical Implications.
| Τίτλος: | The Evolution of Bariatric and Metabolic Surgery in the Artificial Intelligence Era: A Comprehensive Systematic Review of Current Applications and Clinical Implications. |
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| Συγγραφείς: | Foula MS; Department of Surgery, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. msfoula@iau.edu.sa., Bamalan OA; General Surgery Department, Security Forces Hospital, Dammam, Saudi Arabia., Foula A; Department of Anesthesia and Perioperative Medicine, King Fahad Specialist Hospital - Dammam, Dammam, Saudi Arabia., Eldamati AM; Department of Surgery, Faculty of Medicine, Alexandria University, Alexandria, Egypt., Alratrout HA; Department of Surgery, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia., AlQatari AA; Department of Surgery, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia., Alsaleem HA; Department of Surgery, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia., Aldabaeab AE; Department of Surgery, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia., Foula H; Department of Nephrology, Alqabbary Specialist Hospital, Alexandria, Egypt., Zakaria HM; Department of Surgery, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia., Alarfaj MA; Department of Surgery, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia., Sharroufna MJ; Department of Surgery, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia., Nwesar FA; Department of Obstetrics and Gynecology, Mouwasat Hospital, Dammam, Saudi Arabia. |
| Πηγή: | Obesity surgery [Obes Surg] 2026 Oct; Vol. 36 (10), pp. 5478-5497. Date of Electronic Publication: 2026 Aug 20. |
| Τύπος έκδοσης: | Systematic Review; Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer Science + Business Media Country of Publication: United States NLM ID: 9106714 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1708-0428 (Electronic) Linking ISSN: 09608923 NLM ISO Abbreviation: Obes Surg Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2006- : New York : Springer Science + Business Media Original Publication: Oxford, OX, UK : Rapid Communications of Oxford, [1991- |
| Ιατρικοί όροι (MeSH): | Bariatric Surgery*/methods , Bariatric Surgery*/trends , Artificial Intelligence*, Humans ; Generative Artificial Intelligence |
| Περίληψη: | Background: Artificial intelligence (AI) uses in the field of bariatric and metabolic surgery (BMS) have evolved over the past few years. However, published evidence remains limited. AI generally refers to computer systems capable of performing tasks that require human intelligence and cognitive functions. Objective: This systematic review aims to evaluate the utility of AI tools in BMS and summarize current applications to establish a common language for bariatric surgeons. Methods: A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Cochrane Handbook for Systematic Reviews of Interventions guidelines. Studies indexed in PubMed, Cochrane, and Web of Science databases were identified using appropriate keywords related to obesity, bariatric procedures, and AI tools from inception until October 2025. Relevant studies' reference lists were also screened. The quality of included studies was assessed using the Joanna Briggs Institute critical appraisal tools. Results: The literature search identified 840 studies, with an additional 25 studies identified through citation screening. After removing duplicates and screening, 88 studies were included, categorized into preoperative, intraoperative, and postoperative AI uses as well as large language models uses. Quality assessment showed overall moderate to high quality. Conclusion: AI uses in BMS remains a supportive rather than a decisive tool. When applied appropriately, AI can assist surgeons and patients. LLMs should be used cautiously for preliminary health education. Societal initiatives should direct future efforts to address research gaps. Improving surgeon AI literacy, collaboration with AI specialists, and establishing national registries will maximize AI's potential. (© 2026. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.) |
| Competing Interests: | Declarations. Competing Interests: The authors declare no competing interests. |
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| Contributed Indexing: | Keywords: Artificial intelligence; Bariatric surgery; Deep learning; Gastric bypass; Generative AI; Machine learning; Obesity |
| Entry Date(s): | Date Created: 20260820 Date Completed: 20261007 Latest Revision: 20261010 |
| Update Code: | 20261010 |
| DOI: | 10.1007/s11695-026-08908-7 |
| PMID: | 42622983 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1708-0428 |
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| DOI: | 10.1007/s11695-026-08908-7 |