ArticlePediatric cardiology2024
Progression of an Artificial Intelligence Chatbot (ChatGPT) for Pediatric Cardiology Educational Knowledge Assessment.
Article in Pediatric cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis 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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Language Models in Cardiology: Systematic Review.JMIR cardio · 2026Pooled it
- Assessment of ChatGPT-4.0 versus ChatGPT-Mini in Generating Guideline-Based Hypertension Content.Arquivos brasileiros de cardiologia · 2026Article
- Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review.JMIR medical education · 2025Article
- Performance of DeepSeek and GPT Models on Pediatric Board Preparation Questions: Comparative Evaluation.JMIR AI · 2025Article
- Systematic Review on Large Language Models in Orthopaedic Surgery.Journal of clinical medicine · 2025Review
- Accuracy of ChatGPT in answering cardiology board-style questions.Journal of educational evaluation for health professions · 2025Article
- Comparing closed and open large language models on pediatric cardiology board exam performance.Annals of pediatric cardiologyArticle
- Artificial intelligence in pediatric cardiology: Where do we stand in 2024?Annals of pediatric cardiologyArticle
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
Artificial intelligence chatbots, like ChatGPT, have become powerful tools that are disrupting how humans interact with technology. The potential uses within medicine are vast. In medical education, these chatbots have shown improvements, in a short time span, in generalized medical examinations. We evaluated the overall performance and improvement between ChatGPT 3.5 and 4.0 in a test of pediatric cardiology knowledge. ChatGPT 3.5 and ChatGPT 4.0 were used to answer text-based multiple-choice questions derived from a Pediatric Cardiology Board Review textbook. Each chatbot was given an 88 question test, subcategorized into 11 topics. We excluded questions with modalities other than text (sound clips or images). Statistical analysis was done using an unpaired two-tailed t-test. Of the same 88 questions, ChatGPT 4.0 answered 66% of the questions correctly (n = 58/88) which was significantly greater (p < 0.0001) than ChatGPT 3.5, which only answered 38% (33/88). The ChatGPT 4.0 version also did better on each subspeciality topic as compared to ChatGPT 3.5. While acknowledging that ChatGPT does not yet offer subspecialty level knowledge in pediatric cardiology, the performance in pediatric cardiology educational assessments showed a considerable improvement in a short period of time between ChatGPT 3.5 and 4.0.
Indexed as
Identifiers
38170274What 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.