ArticlePLOS digital health2025
AI-assisted assessment of the IFSO consensus on obesity management medications in the context of metabolic bariatric surgery.
Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- A Controlled Comparison of Human and AI-Assisted Automated Revision of Delphi Statements on RNA-Based Medicines: Parallel, 2-Arm Study.JMIR medical informatics · 2026Article
- IFSO Survey: Use of Large Language Models (LLMs) by Metabolic Bariatric Surgeons and Integrated Health Professionals.Obesity surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and large language models (LLMs), when combined with human expertise in collaborative intelligence (CI), can enhance medical decision-making, reduce bias in guideline development, and support precision care. New obesity management medications (OMMs) such as GLP-1 receptor agonists and dual incretin mimetics complement metabolic bariatric surgery but currently lack clear integration strategies. To address this gap, IFSO released consensus guidelines in 2024. This study evaluates their robustness by comparing expert recommendations with LLM outputs, highlighting the role of AI in assessment and strengthening clinical consensus. Thirty-one IFSO consensus statements were tested across eleven advanced LLMs on June 1, 2025. Models received standardized prompts that required binary "AGREE" or "DISAGREE" outputs, supported by brief, evidence-based rationales. Individual responses were aggregated to form an overall "LLM consensus," and mean percentage agreement was calculated against the original IFSO expert grades-Fleiss' κappa quantified inter-model reliability beyond chance. Incorporating the AI responses led to shifts in the consensus grade for 2 of the 31 statements. One statement originally rated A + was downgraded to A after some LLMs' outputs indicated disagreement, citing nuanced evidence on pre- and post-MBS OMM use and comparative effectiveness. One statement on combining OMMs with endoscopic therapies was upgraded from C to B due to unanimous support from the LLM. The remaining 29 statements maintained their original grades, demonstrating strong overall alignment between LLM outputs and expert consensus. Overall concordance between LLMs and experts was 93%, with substantial inter-model agreement(κ = 0.81 [95% CI 0.74-0.87]). Integrating AI, especially LLMs, into collaborative intelligence frameworks strengthens clinical consensus when evidence is limited. This study shows that concordance between LLMs outputs and expert consensus should not be taken as evidence of objectivity; rather, it may simply reflect overlap between the published evidence base and the model's training data or retrieval sources.
Identifiers
What OpenQuestion 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 OpenQuestion graph.