ArticleMayo Clinic proceedings. Digital health2025
Development and Evaluation of an Artificial Intelligence-Powered Surgical Oral Examination Simulator: A Pilot Study.
Article in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Attitudes Toward Large Language Models in Health Care and Preferences for Their Adoption and Oversight Among Health Care Professionals: Cross-Sectional Survey.Journal of medical Internet research · 2026Article
- Educational Impact of Artificial Intelligence-Navigation Surgery on Anatomical Landmark Recognition in Medical Students.Annals of gastroenterological surgery · 2026Article
- Large Language Model Performance and Clinical Reasoning Tasks.JAMA network open · 2026Article
- Review
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
Abstract
Objective: To develop and validate an artificial intelligence-powered platform that simulates surgical oral examinations, addressing the limitations of traditional faculty-led sessions. Patients and Methods: This cross-sectional study, conducted from June 1, 2024, through December 1, 2024, comprised technical validation and educational assessment of a novel large language model (LLM)-based surgical education tool (surgery oral examination large language model [SOE-LLM]). The study involved 12 surgical clerkship students completing their core rotation at a major academic medical center. The SOE-LLM, using MIMIC-IV-derived surgical cases (acute appendicitis and pancreatitis), was implemented to simulate oral examinations. Technical validation assessed performance across 8 domains: case presentation accuracy, physical examination findings, historical detail preservation, laboratory data reporting, imaging interpretation, management decisions, and recognition of contraindicated interventions. Educational utility was evaluated using a 5-point Likert scale. Results: Technical validation showed the SOE-LLM's ability to function as a consistent oral examiner. The model accurately guided students through case presentations, responded to diagnostic questions, and provided clinically sound responses based on MIMIC-IV cases. When tested with standardized prompts, it maintained examination fidelity, requiring proper diagnostic reasoning and differentiating operative versus medical management. Student evaluations highlighted the platform's value as an examination preparation tool (mean, 4.250; SEM, 0.1794) and its ability to create a low-stakes environment for high-stakes decision practice (mean, 4.833; SEM, 0.1124). Conclusion: The SOE-LLM shows potential as a valuable tool for surgical education, offering a consistent and accessible platform for simulating oral examinations.
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.