ArticleCureus2025
Performance of ChatGPT-3.5 and ChatGPT-4 in Solving Questions Based on Core Concepts in Cardiovascular Physiology.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Article
- Guiding student use of generative AI in undergraduate pharmacology: a narrative review and proposed source-checking process framework.Frontiers in medicine · 2026Review
- Evaluating large language models as graders of medical short answer questions: a comparative analysis with expert human graders.Medical education online · 2025Article
- Performance of ChatGPT and DeepSeek in the Management of Postprostatectomy Urinary IncontinenceInternational braz j urol : official journal of the Brazilian Society of UrologyArticle
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Medical students often struggle to apply previously learned concepts to new situations, such as cardiovascular physiology. ChatGPT, an AI chatbot trained through deep learning, can analyze basic problems and produce human-like language in various subjects. Multiple-choice questions (MCQs) are given to students by many medical schools before exams, but due to time constraints, instructors frequently lack the resources necessary to adequately explain the practice questions. Even when given, the explanations might not give students sufficient information to grasp the concepts completely. This study aimed to examine ChatGPT's ability to solve various reasoning problems based on the core concepts of cardiovascular physiology. Materials and methods Multiple-choice questions were presented manually to both chatbots (ChatGPT-4 and ChatGPT-3.5), and the answers generated were compared with the faculty-led answer key using various statistical tests. Results The accuracy rates of ChatGPT-4 and ChatGPT-3.5 were 83.33% and 60%, respectively, which were statistically significant. Compared to ChatGPT-3.5, ChatGPT-4's explanation of the response was substantially more appropriate. The execution of ChatGPT-4 was better than ChatGPT-3.5 in certain core concept areas like mass balance (75% vs. 50%), scientific reasoning (60% vs. 40%), and homeostasis (100% vs. 66.67%). Conclusion When it came to responding to concept-based questions about cardiovascular physiology, ChatGPT-4 outperformed ChatGPT-3.5. However, to ensure accuracy, faculty members should review the generated explanations, and thus, the growing application of generative AI in the form of virtual-assisted learning approaches in medical education needs to be carefully considered.
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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.