ArticleSurgical endoscopy2024
Artificial intelligence large language model scores highly on focused practice designation in metabolic and bariatric surgery board practice questions.
Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Evolution of Bariatric and Metabolic Surgery in the Artificial Intelligence Era: A Comprehensive Systematic Review of Current Applications and Clinical Implications.Obesity surgery · 2026Pooled it
- Large language models in obesity: a systematic review.International journal of obesity (2005) · 2026Pooled it
- Article
- Perspectives of Patients Seeking Bariatric Surgery: The Impact of Early Patient-Provider Communication on Bariatric Surgery Utilization.Obesity surgery · 2025Article
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
backgroundArtificial intelligence models such as ChatGPT (Open AI) have performed well on the exams of various medical and surgical fields. It is not yet known how ChatGPT performs on similar metabolic and bariatric surgery (MBS) questions.
objectiveAssess the performance of ChatGPT on Focused Practice Designation in Metabolic and Bariatric Surgery board-style questions.
settingUnited States.
methodsQuestions obtained from the largest commercially available bank of FPD-MBS practice questions were entered into ChatGPT-4, as is, without prior training. We assessed the overall percentage correct as well as the percentage correct within each of the five American Board of Surgery (ABS) question categories. One-way ANOVA was used to determine if the frequency of correct answers differed between categories.
resultsOut of 255 questions, ChatGPT-4 correctly answered 189 (74.1%). Between the five question categories there was no difference between the frequency of correct answers (p = 0.22). It did not matter if questions were entered individually or in groups of up to 10.
conclusionWithout prior training, ChatGPT-4 scored highly when evaluated on the largest practice question bank for the FPD-MBS exam.
Indexed as
Identifiers
39317906What 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.