SynthesisLangenbeck's archives of surgery2026
Artificial intelligence and machine learning in bariatric surgery: a comprehensive systematic review.
Synthesis in Langenbeck's archives of surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial intelligence in obesity management: clinical evidence, translational gaps, and implementation priorities-a structured narrative review.Frontiers in endocrinology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) and machine learning (ML) are increasingly integrated into metabolic and bariatric surgery (MBS), offering opportunities to enhance decision-making, optimize perioperative care, and personalize outcomes. However, their clinical adoption requires a comprehensive understanding of current applications and limitations.
objectivesTo systematically review the use of AI and ML across the bariatric surgery pathway, from preoperative planning to postoperative follow-up.
methodsA systematic search of PubMed and Embase (last accessed August 28, 2025) identified original studies applying AI/ML in MBS. Inclusion criteria were studies involving bariatric patients and AI/ML-based models for clinical or perioperative purposes. Data on study design, sample size, surgical procedure, AI/ML technique, and primary outcomes were extracted. Studies were categorized into preoperative, intraoperative, and postoperative phases.
resultsOf 142 records screened, 27 studies met inclusion criteria. Preoperative applications focused on patient selection, anatomical prediction, and risk stratification, including models predicting weight-loss success, gastroesophageal reflux disease, and hiatal hernia. Intraoperative research explored operative time forecasting, workflow optimization, and automated video analysis for skill assessment and step recognition. Postoperative models addressed complication prediction, nutritional surveillance, and long-term weight trajectory forecasting. Reported accuracy was high (AUC up to 0.93), but external validation and fairness audits were limited. Emerging evidence also supports AI-driven educational tools, including large language models for surgical training.
conclusionsAI is rapidly transforming bariatric surgery across the preoperative, intraoperative, and postoperative pathway, offering unprecedented opportunities for personalization, efficiency, and quality improvement. From risk prediction to skill assessment and long-term outcome forecasting, its applications can augment, rather than replace, human expertise. Ethical safeguards, transparency, and equitable access remain critical for safe and effective integration.
Indexed as
Identifiers
What 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.