ArticleLa Radiologia medica2024
How do large language models answer breast cancer quiz questions? A comparative study of GPT-3.5, GPT-4 and Google Gemini.
Article in La Radiologia medica, 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
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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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Performance of Seven Large Language Models on Anatomy Examination Questions.Clinical anatomy (New York, N.Y.) · 2026Article
- Evaluation of ChatGPT-4o in Breast Cancer Screening: Insights from the 5th Edition BI-RADS Atlas and ACR Guidelines.Journal of imaging informatics in medicine · 2026Article
- Insufficient reporting quality in large language model studies in the field of radiology.Insights into imaging · 2026Article
- Large language models in clinical psychiatry: Applications and optimization strategies.World journal of psychiatry · 2025Review
- Temporal evolution of large language models (LLMs) in oncology.Journal of translational medicine · 2025Article
- Generative AI and large language models in nuclear medicine: current status and future prospects.Annals of nuclear medicine · 2024Review
- Evaluating Accuracy and Readability of Responses to Midlife Health Questions: A Comparative Analysis of Six Large Language Model Chatbots.Journal of mid-life healthArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Applications of large language models (LLMs) in the healthcare field have shown promising results in processing and summarizing multidisciplinary information. This study evaluated the ability of three publicly available LLMs (GPT-3.5, GPT-4, and Google Gemini-then called Bard) to answer 60 multiple-choice questions (29 sourced from public databases, 31 newly formulated by experienced breast radiologists) about different aspects of breast cancer care: treatment and prognosis, diagnostic and interventional techniques, imaging interpretation, and pathology. Overall, the rate of correct answers significantly differed among LLMs (p = 0.010): the best performance was achieved by GPT-4 (95%, 57/60) followed by GPT-3.5 (90%, 54/60) and Google Gemini (80%, 48/60). Across all LLMs, no significant differences were observed in the rates of correct replies to questions sourced from public databases and newly formulated ones (p ≥ 0.593). These results highlight the potential benefits of LLMs in breast cancer care, which will need to be further refined through in-context training.
Indexed as
Identifiers
39138732What 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.