Evidence map›Paper›PMID 40688974›Full record

ArticleCureus2025

Evaluating the Use of ChatGPT 3.5 and Bard as Self-Assessment Tools for Short Answer Questions in Undergraduate Ophthalmology.

Abhijeet M Khake, Suvarna Gokhale, Pradeep Dindore, Sonali Khake, Manjiri Desai

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Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Abhijeet M KhakeDepartment of Ophthalmology, Pacific Medical College and Hospital, Udaipur, IND.
Suvarna GokhaleDepartment of Ophthalmology, Smt. Kashibai Navale Medical College and General Hospital, Pune, IND.
Pradeep DindoreDepartment of Ophthalmology, Prakash Institute of Medical Sciences and Research, Uran Islampur, IND.
Sonali KhakeDepartment of Anatomy, SSPM Medical College and Lifetime Hospital, Sindhudurg, IND.
Manjiri DesaiDepartment of Community Medicine, D. Y. Patil Medical College, Kolhapur, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to evaluate the efficacy of ChatGPT 3.5 and Google Bard as tools for self-assessment of short answer questions (SAQs) in ophthalmology for undergraduate medical students. METHODOLOGY: A total of 261 SAQs were randomly selected from previous university examination papers and publicly available ophthalmology question banks. The questions were classified according to the competency-based medical education (CBME) curriculum of the National Medical Commission (NMC) of India into three categories: short note task-oriented questions (SNTO, n = 169), short note reasoning questions (SNRQ, n = 15), and applied aspect SAQs (SN Applied, n = 77). Image-based questions were excluded. Three ophthalmologists collaboratively developed model answers for each question. The same questions were then submitted to ChatGPT 3.5 and Google Bard. The AI-generated responses were independently evaluated by three ophthalmologists using a 3-point scale based on correct diagnosis, accuracy of content, and relevance. The scores were compiled, and the data were analyzed to compare the overall and category-wise performance of the two AI tools.

resultsOut of a total possible score of 783 (261 questions × 3 points), ChatGPT 3.5 scored 696 (88.8%), while Bard scored 685 (87.5%). Although the overall performance difference was insignificant, ChatGPT 3.5 performed significantly better in the SNTO category. However, both AI tools produced poor-quality or inadequate answers for a subset of questions: 50 (19%) by ChatGPT 3.5 and 44 (16.8%) by Bard. Some responses lacked essential information, even for high-yield topics.

conclusionChatGPT 3.5 and Bard can generate accurate and relevant responses to ophthalmology SAQs in most cases. ChatGPT 3.5 demonstrated slightly better performance, particularly for task-oriented questions, suggesting it may be a more effective tool for undergraduate students' self-assessment. However, due to a notable error rate (~20%), AI-generated responses should not be used in isolation and must be cross-referenced with standard textbooks. These tools best suit rapid information retrieval during the early study phases.

Indexed as

artificial intelligencechatgpt 3.5deep learninglarge language modelsophthalmologyself-assessmentshort answer questionsundergraduate medical education

Identifiers

PMID40688974
PMCPMC12273740

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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.