Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 16, 2025

Subjective Refraction Test Using a Smartphone for Vision Screening
05:36

Subjective Refraction Test Using a Smartphone for Vision Screening

Published on: October 18, 2024

647

Learning the Randleman Criteria in Refractive Surgery: Utilizing ChatGPT-3.5 Versus Internet Search Engine.

Jared J Tuttle1, Majid Moshirfar2,3,4, James Garcia1

  • 1Ophthalmology, University of Texas Health Science Center at San Antonio, San Antonio, USA.

Cureus
|August 19, 2024
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Virtual Reality in Ophthalmic Surgical Education: Current Innovations and Future Perspectives [Letter].

Clinical ophthalmology (Auckland, N.Z.)·2026
Same author

Efficacy of the TuttleNumbNow anesthesia protocol for mandibular first molars: a retrospective study.

Journal of dental anesthesia and pain medicine·2026
Same author

Current capabilities of large language models as peer reviewers for manuscripts submitted to ophthalmology-related journals.

Medicine·2026
Same author

Mucus Fishing Syndrome: Case Series and a Narrative Review of Literature.

Ophthalmology and therapy·2026
Same author

Reply: Optimizing Keratorefractive Lenticule Extraction: Addressing Centration, Epithelial Remodeling, and Nomogram Refinement.

Journal of refractive surgery (Thorofare, N.J. : 1995)·2026
Same author

A Novel Missense Mutation in a Compound Heterozygous Patient with Fish-Eye Disease: A Case Report.

Case reports in ophthalmology·2026
Same journal

Retraction: The Association Between Janus Kinase 2 and Factor V Leiden Mutations and Thrombotic Complications in Patients With Myeloproliferative Disorders: A Study From Saudi Arabia.

Cureus·2026
Same journal

Patient-Reported Understanding of Emergency Department Discharge Instructions, Satisfaction, and Acceptability of a Future Telemedicine-Based Call-Back Program: Phase 1 Observational Pilot Study at a Tertiary Hospital in Dubai, United Arab Emirates.

Cureus·2026
Same journal

Correction: Methylprednisolone-Induced Delayed and Sustained Bradycardia in Multisystem Inflammatory Syndrome in Children.

Cureus·2026
Same journal

Rapid Interval Development of a Left Hepatic Artery Pseudoaneurysm During Acute Interstitial Edematous Pancreatitis.

Cureus·2026
Same journal

Recurrent Acute Pancreatitis Secondary to Untreated Hyperparathyroidism: A Case Report and Literature Review.

Cureus·2026
Same journal

A Prospective Case-Control Study of Helicobacter pylori and Systemic Inflammation in Colorectal Cancer Pathogenesis.

Cureus·2026
See all related articles
This summary is machine-generated.

Large language models like ChatGPT-3.5 are less accurate than internet searches for medical information. Internet searches reliably defined the Randleman criteria, while ChatGPT-3.5 often provided incorrect or fabricated details.

Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Information Retrieval

Background:

  • Large language models (LLMs) show promise for self-directed medical learning.
  • Concerns exist regarding the accuracy of LLMs, such as ChatGPT-3.5, in specialized medical fields.
  • The Randleman criteria require precise definition for accurate medical application.

Purpose of the Study:

  • To compare the accuracy and reliability of ChatGPT-3.5 versus internet search engines.
  • To evaluate the efficacy of these tools in defining the Randleman criteria within a self-directed learning context.
  • To assess the efficiency and truthfulness of information provided by ChatGPT-3.5 and internet searches.

Main Methods:

  • Twenty-three medical students used ChatGPT-3.5 for 10 minutes, then conducted independent internet searches for 10 minutes.
Keywords:
artificial intelligence and educationchatgpt-3.5corneal and refractive surgerykeratoconuslasikmedical educationpost-lasik ectasiaprompt engineeringrandleman criteriaself-directed learning (sdl)

More Related Videos

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

6.3K
Author Spotlight: Advancements in Refractive Surgical Correction for Presbyopia and Exploring Postoperative Visual Acuity
05:46

Author Spotlight: Advancements in Refractive Surgical Correction for Presbyopia and Exploring Postoperative Visual Acuity

Published on: September 20, 2024

392

Related Experiment Videos

Last Updated: Jun 16, 2025

Subjective Refraction Test Using a Smartphone for Vision Screening
05:36

Subjective Refraction Test Using a Smartphone for Vision Screening

Published on: October 18, 2024

647
Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

6.3K
Author Spotlight: Advancements in Refractive Surgical Correction for Presbyopia and Exploring Postoperative Visual Acuity
05:46

Author Spotlight: Advancements in Refractive Surgical Correction for Presbyopia and Exploring Postoperative Visual Acuity

Published on: September 20, 2024

392
  • Information gathered on the Randleman criteria from both sources was analyzed.
  • Analyses focused on accuracy, efficiency, and reliability of the definitions and parameters provided.

    • Main Results:

    • Internet searches provided correct Randleman criteria definitions for 100% of students.
    • ChatGPT-3.5 correctly defined the criteria for only 26.1% of students.
    • ChatGPT-3.5 generated incorrect definitions (17.4%), fabricated information (4.3%), and failed to define criteria for 52.2% of students.

    Conclusions:

    • Internet search engines are superior to ChatGPT-3.5 for accessing accurate and reliable medical information like the Randleman criteria.
    • ChatGPT-3.5 demonstrated significant inaccuracies, including fabricated data and incomplete definitions, hindering self-directed learning.
    • Medical learners must exercise caution and critical judgment when utilizing LLMs; further research into prompt engineering and LLM updates is warranted.