ArticleScientific reports2024
The model student: GPT-4 performance on graduate biomedical science exams.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological data.Nature methods · 2026Article
- Evaluation of large language models in medical examinations: A scoping review protocol.PloS one · 2026Article
- A Multiagent Summarization and Auto-Evaluation Framework for Medical Text: Development and Evaluation Study.JMIR AI · 2025Article
- GPT-4 shows comparable performance to human examiners in ranking open-text answers.Scientific reports · 2025Article
- Article
- Towards a World Wide Web powered by generative AI.Scientific reports · 2025Article
- Is your curriculum GenAI-proof? A method for GenAI impact assessment and a case study.MedEdPublish (2016) · 2025Article
- Leveraging large language models for spelling correction in Turkish.PeerJ. Computer science · 2025Article
- Comparative performance of large language models for patient-initiated ophthalmology consultations.Frontiers in public health · 2025Article
- Article
- Large language models in biomedicine and health: current research landscape and future directions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Education and Training Assessment and Artificial Intelligence. A Pragmatic Guide for Educators.British journal of biomedical science · 2024Review
- Current and future applications of artificial intelligence in surgery: implications for clinical practice and research.Frontiers in surgery · 2024Review
- The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials.Frontiers in neurology · 2024Review
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
The GPT-4 large language model (LLM) and ChatGPT chatbot have emerged as accessible and capable tools for generating English-language text in a variety of formats. GPT-4 has previously performed well when applied to questions from multiple standardized examinations. However, further evaluation of trustworthiness and accuracy of GPT-4 responses across various knowledge domains is essential before its use as a reference resource. Here, we assess GPT-4 performance on nine graduate-level examinations in the biomedical sciences (seven blinded), finding that GPT-4 scores exceed the student average in seven of nine cases and exceed all student scores for four exams. GPT-4 performed very well on fill-in-the-blank, short-answer, and essay questions, and correctly answered several questions on figures sourced from published manuscripts. Conversely, GPT-4 performed poorly on questions with figures containing simulated data and those requiring a hand-drawn answer. Two GPT-4 answer-sets were flagged as plagiarism based on answer similarity and some model responses included detailed hallucinations. In addition to assessing GPT-4 performance, we discuss patterns and limitations in GPT-4 capabilities with the goal of informing design of future academic examinations in the chatbot era.
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What 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.