Artificial Intelligence as a Decision-Making Tool for Bariatric Surgeries in the Preoperative Assessment: A Bibliometric Analysis
Main Article Content
Abstract
Introduction: The use of artificial intelligence (AI) in medicine is becoming increasingly common, significantly altering medical practices. Objective: To analyze whether Artificial Intelligence can be an effective tool ou can support for the preoperative period of bariatric surgeries, helping both in decision-making to be carried out by the doctor and in predicting diseases that the patient is more likely to develop in the postoperative period. Methods: A systematic review was conducted, seeking studies published between 2011 and 2023, in English. The databases used were PubMed and Web of Science. Results: Most of the selected articles discussed the use of Machine Learning to assist in the preoperative period of bariatric surgeries, analyzing the general use of AI in this type of surgery and its contribution to the decision-making process, in addition to its use to predict the behavior of diseases that may arise in patients after bariatric surgery. Conclusion: Artificial Intelligence is being increasingly used in the medical field, including in bariatric surgery, where it helps in decision-making for faster and more accurate procedures, reducing errors, and promoting faster patient recovery, confirming the acceptance of the hypothesis of this study.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY) that allows others to share and adapt the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.References
1. Barbour AB, Frush JM, Gatta LA, McManigle WC, Keah NM, Bejarano-Pineda L, et al. Artificial intelligence in health care: insights from an educational forum. J Med Educ Curric Dev. 2020;6:2382120519889348. https://doi.org/10.1177/2382120519889348
2. Kong SC, Cheung WMY, Zhang G. Evaluating an artificial intelligence literacy Programme for Developing University Students’ conceptual understanding, literacy, empowerment, and ethical awareness. Educ Technol Soc. 2023;26(1):16-30. https://doi.org/10.1007/s10639-022-11408-7
3. Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. Ieee Access. 2017;6:9375-89. http://doi.org/10.1109/ACCESS.2017.2788044
4. Lima MLDF, Souza SMF, Sá IVD, Santana OA. Deep learning with aerial surveys for extensive livestock hotspot recognition in the Brazilian Semi-arid Region. Cienc Agrotec. 2023;47:e010922. https://doi.org/10.1590/1413-7054202347010922
5. Wu W, Deng. Analysis of Public Opinion in Colleges and Universities Based on Wireless Web Crawler Technology in the Context of Artificial Intelligence. Mobile Inf Syst. 2022;7745028. https://doi.org/10.1155/2022/7745028
6. Wang J. A, Liu S, Zhang X. Application of artificial intelligence in university sports risk recognition and identification. J Intelligent Fuzzy Syst. 2021;40(2):3361-72. https://doi.org/10.3233/JIFS-189375
7. Xing L, Goetsch S, Cai J. Point/Counterpoint. Artificial intelligence should be part of medical physics graduate program curriculum. Med Phy. 2021;48(4):1457-60. https://doi.org/10.1002/mp.14587
8. Sheng L. Popularization of science in colleges and universities in the new network media environment based on artificial intelligence. Soft Computing, 2023;27:10213-23. https://doi.org/10.1007/s00500-023-08268-9
9. Moher D, Stewart L, Shekelle P. All in the family: systematic reviews, rapid reviews, scoping reviews, realist reviews, and more. Syst Rev. 2015;4:183. https://doi.org/10.1186/s13643-015-0163-7
10. Dermeval D, Coelho JAPM, Bittencourt II. Mapeamento sistemático e revisão sistemática da literatura em informática na educação. In: Jaques PA, Siqueira S, Bittencourt I, Pimentel M. Metodologia de Pesquisa Científica em Informática na Educação: Abordagem Quantitativa. V.2. Porto Alegre: SBC, 2020.
11. Nascimento JB, Rodrigues RL, Andrade VLVX. Aplicações de game learning analytics na abordagem sobre conceitos de matemática. Rev Novas Tecnol Educ. 2021;19(2):51-60. https://doi.org/10.22456/1679-1916.121186
12. Ramos‐Rodríguez AR, Ruíz‐Navarro J. Changes in the intellectual structure of strategic management research: A bibliometric study of the Strategic Management Journal, 1980-2000. Strat Mgmt J. 2004;25(10):981-1004. https://doi.org/10.1002/smj.397
13. Miyahira SA, Azevedo JLMC, Araújo E. Fuzzy obesity index (MAFOI) for obesity evaluation and bariatric surgery indication. J Transl Med. 2011;9:134. https://doi.org/10.1186/1479-5876-9-134
14. Johnston SS, Morton JM, Kalsekar I, Ammann EM, Hsiao CW, Reps J. Using machine learning applied to real-world healthcare data for predictive analytics: an applied example in bariatric surgery. Value Health. 2019;22(5):580-6. https://doi.org/10.1016/j.jval.2019.01.011
15. Wise ES, Amateau SK, Ikramuddin S, Leslie DB. Prediction of thirty-day morbidity and mortality after laparoscopic sleeve gastrectomy: data from an artificial neural network. Surg Endosc. 2020;34(8):3590-6. https://doi.org/10.1007/s00464-019-07130-0
16. Cao Y, Näslund I, Näslund E, Ottosson J, Montgomery S, Stenberg E. Using a convolutional neural network to predict remission of diabetes after gastric bypass surgery: machine learning study from the scandinavian obesity surgery register. JMIR Med Inform. 2021;9(8):e25612. https://doi.org/10.2196/25612
17. Aminian A, Zajichek A, Arterburn DE, Wolski KE, Brethauer SA, Schauer PR, et al. Predicting 10-year risk of end-organ complications of type 2 diabetes with and without metabolic surgery: a machine learning approach. Diabetes Care. 2021;43(4):852-9. https://doi.org/10.2337/dc19-2057
18. Pantelis AG, Stravodimos GK, Lapatsanis DP. A scoping review of artificial intelligence and machine learning in bariatric and metabolic surgery: current status and future perspectives. Obes Surg. 2021;31(10):4555-63. https://doi.org/10.1007/s11695-021-05548-x
19. Emile SH, Ghareeb W, Elfeki H, El Sorogy M, Fouad A, Elrefai M. Development and validation of an artificial intelligence-based model to predict gastroesophageal reflux disease after sleeve gastrectomy. Obes Surg. 2022;32(8):2537-47. https://doi.org/10.1007/s11695-022-06112-x
20. Bektaş M, Reiber BM, Pereira JC, Burchell GL, van der Peet DL. Artificial intelligence in bariatric surgery: current status and future perspectives. Obes Surg. 2022;32(8):2772-83. https://doi.org/10.1007/s11695-022-06146-1
21. Pantelis AG. Metabolomics in bariatric and metabolic surgery research and the potential of deep learning in bridging the gap. Metabolites. 2022;12(5):458. https://doi.org/10.3390/metabo12050458
22. Liu Y, Sheng C, Feng W, Sun F, Zhang J, Chen Y, et al. A multi-center study on glucometabolic response to bariatric surgery for different subtypes of obesity. Front Endocrinol (Lausanne). 2022;13:989202. https://doi.org/10.3389/fendo.2022.989202
23. Bellini V, Valente M, Turetti M, Del Rio P, Saturno F, Maffezzoni M, et al. Current Applications of Artificial Intelligence in Bariatric Surgery. Obes Surg. 2022;32(8):2717-33. https://doi.org/10.1007/s11695-022-06100-1
24. Pereira SS, Guimarães M, Monteiro MP. Towards precision medicine in bariatric surgery prescription. Rev Endocr Metab Disord. 2023;24(5):961-77. https://doi.org/10.1007/s11154-023-09801-9
25. Pan Y, Du R, Han X, Zhu W, Peng D, Tu Y, et al. Machine Learning Prediction of Iron Deficiency Anemia in Chinese Premenopausal Women 12 Months after Sleeve Gastrectomy. Nutrients. 2023;15(15):3385. https://doi.org/10.3390/nu15153385
26. Hsu JL, Chen KA, Butler LR, Bahraini A, Kapadia MR, Gomez SM, et al. Application of machine learning to predict postoperative gastrointestinal bleed in bariatric surgery. Surg Endoscopy. 2023; 37(9):7121-7. https://doi.org/10.1007/s00464-023-10156-0
27. Saux P, Bauvin P, Raverdy V, Teigny J, Verkindt H, Soumphonphakdy T, et al. Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study. Lancet Digit Health. 2023;5(10):e692-e702. https://doi.org/10.1016/S2589-7500(23)00135-8
28. Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, et al. A Review of the Role of Artificial Intelligence in Healthcare. J Pers Med. 2023;13(6):951. https://doi.org/10.3390/jpm13060951
29. Bekbolatova M, Mayer J, Ong CW, Toma M. Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives. Healthcare (Basel). 2024;12(2):125. https://doi.org/10.3390/healthcare12020125
30. Saqib M, Iftikhar M, Neha F, Karishma F, Mumtaz H. Artificial intelligence in critical illness and its impact on patient care: a comprehensive review. Front Med (Lausanne). 2023;10:1176192. https://doi.org/10.3389/fmed.2023.1176192
31. Raikar GVS, Raikar AS, Somnache, SN. Advancements in artificial intelligence and machine learning in revolutionising biomarker discovery. Braz J Pharmaceutical Sci. 2023;59:e23146. https://doi.org/10.1590/s2175-97902023e23146
32. Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artif Intell Healthcare. 2020;25-60. https://doi.org/10.1016/B978-0-12-818438-7.00002-2
33. Alowais SA, Alghamdi SS, Alsuhebany N, Algahtani, I. Alshaya A, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23(1):689. https://doi.org/10.1186/s12909-023-04698-z
34. Karalis VD. The Integration of Artificial Intelligence into Clinical Practice. Appl Biosci. 2024;3(1):14-44. https://doi.org/10.3390/applbiosci3010002
35. Giordano C, Brennan M, Mohamed B, Rashidi P, Modave F, Tighe P. Accessing Artificial Intelligence for Clinical Decision-Making. Front Digit Health. 2021; 3:645232. https://doi.org/10.3389/fdgth.2021.645232
36. Ghaffar NN, Kaplanoglu E, Nasab A. Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discov Artif Intell. 2023;3(1):5. https://doi.org/10.1007/s44163-023-00049-5
37. Zhang P, Boulos MNK. Generative AI in Medicine and Healthcare: Promises, Opportunities, and Challenges. Future Internet. 2023;15(9):286. https://doi.org/10.3390/fi15090286
38. Hassan AM, Rajesh A, Asaad M, Nelson JA, Coert JH, Mehrara BJ, et al. Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications. Am Surg. 2023;89(1):25-30. https://doi.org/10.1177/00031348221101488
39. Winkler-Schwartz A, Bissonnette V, Mirchi N, Ponnudurai N, Yilmaz R, Ledwos, et al. Artificial intelligence in medical education: best practices using machine learning to assess surgical expertise in virtual reality simulation. J Surg Educ. 2019;76(6):1681-90. https://doi.org/10.1016/j.jsurg.2019.05.015
40. Bilimoria K, Harish V, McCoy L, Mehta N, Morgado F, Nagaraj S, et al. Training for the future: preparing medical students for the impact of artificial intelligence. Ontario Medical Students Association, 2019.