SynthesisObesity surgery2026
Artificial Intelligence Applications in Endoscopic Sleeve Gastroplasty: A Systematic Review of Preliminary Evidence.
Synthesis in Obesity surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEndoscopic Sleeve Gastroplasty (ESG) is an established minimally invasive bariatric intervention, while artificial intelligence (AI) has been increasingly explored across clinical and procedural domains. However, evidence integrating AI into ESG remains limited and fragmented.
aimTo systematically identify and critically interpret the clinical, technical, and educational applications of AI in ESG.
methodsThis systematic review followed PRISMA guidelines and was prospectively registered in PROSPERO. Searches were conducted in PubMed, Embase, Scopus, Web of Science, and the Cochrane Library. Original studies applying AI to ESG in clinical practice, simulation, or education were included. Given marked heterogeneity, results were synthesized qualitatively with emphasis on clinical interpretability.
resultsFive studies met inclusion criteria. A preoperative model predicting 30-day reintervention, 3.3% in 3,583 patients, showed moderate discrimination, AUC 0.74, supporting risk stratification and targeted early surveillance rather than treatment modification. A multicenter study predicting 12-month weight-loss success demonstrated limited preoperative performance, ROC-AUC ~ 0.60, but improved with early follow-up data, reaching ROC-AUC 0.79-0.88, supporting dynamic monitoring rather than baseline selection. Two simulation-based studies reported high accuracy, up to 1.00, for skill classification and up to 100% for procedural recognition, but were based on small or experimental datasets without clinical validation. One study evaluating AI-generated educational content showed comparable performance to standard materials, with accuracy 3.81 vs. 3.78 and readability 4.11 vs. 4.02. No study demonstrated prospective improvement in patient outcomes or integration into routine clinical workflows.
conclusionCurrent evidence suggests that AI in ESG has selective, task-specific signals of potential utility, particularly in risk stratification, longitudinal follow-up, training, and education, but remains at an early stage of clinical translation. At present, AI should be considered an adjunct to clinical judgment rather than a tool for autonomous decision-making, and further prospective validation is required before routine implementation.
Indexed as
Identifiers
42265407What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.