ArticleCureus2024
Assessing the Responses of Large Language Models (ChatGPT-4, Gemini, and Microsoft Copilot) to Frequently Asked Questions in Breast Imaging: A Study on Readability and Accuracy.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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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
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of the readability of ChatGPT and Bard in medical communication: a meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- AI-Assisted Early Clinical Diagnosis of External Auditory Canal Squamous Cell Carcinoma: A Rare Case in a Young Male From Pakistan.Clinical case reports · 2026Article
- Evaluating the quality of artificial intelligence responses to psoriasis-related clinical and patient questions: a comparative study of ChatGPT, Gemini, and Microsoft Copilot.Proceedings (Baylor University. Medical Center) · 2026Article
- Evaluation of large language model responses to patient questions on oral anticoagulant therapy: a comparative expert assessment.Exploratory research in clinical and social pharmacy · 2026Article
- Are large language models such as ChatGPT, capable of supporting patients and general practitioners after spine surgery?European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Evaluating artificial intelligence chatbot performance on board-level geriatrics questions.Scientific reports · 2026Article
- Performance of 5 AI Models on United States Medical Licensing Examination Step 1 Questions: Comparative Observational Study.JMIR AI · 2026Article
- Evaluating search-enabled large language model interfaces for mpox public health consultation: a guideline-based comparative study.Frontiers in public health · 2026Article
- Evaluation of artificial language-adjusted readability on cataract surgery queries.Digital journal of ophthalmology : DJO · 2026Article
- Comparative analysis of the effectiveness of microsoft copilot artificial intelligence chatbot and google search in answering patient inquiries about infertility: evaluating readability, understandability, and actionability.International journal of impotence research · 2025Article
- Evaluation of large language models in patient education for hyperthyroidism: A comparative study of chatgpt, gemini, and deepseek.Endocrine · 2025Article
- Large language models as information providers for appropriate antimicrobial use: computational text analysis and expert-rated comparison of ChatGPT, Claude and Gemini.BMJ health & care informatics · 2025Article
- Five advanced chatbots solving European Diploma in Radiology (EDiR) text-based questions: differences in performance and consistency.European radiology experimental · 2025Article
- ChatGPT and Microsoft Copilot for Cochlear Implant Side Selection: A Preliminary Study.Audiology research · 2025Article
- Article
- Performance of artificial intelligence chatbots in responding to the frequently asked questions of patients regarding dental prostheses.BMC oral health · 2025Article
- Microsoft Copilot Provides More Accurate and Reliable Information About Anterior Cruciate Ligament Injury and Repair Than ChatGPT and Google Gemini; However, No Resource Was Overall the Best.Arthroscopy, sports medicine, and rehabilitation · 2025Article
- Accuracy of LLMs in medical education: evidence from a concordance test with medical teacher.BMC medical education · 2025Article
- Evaluating text and visual diagnostic capabilities of large language models on questions related to the Breast Imaging Reporting and Data System Atlas 5Diagnostic and interventional radiology (Ankara, Turkey) · 2025Observational
- Diagnostic performance of four AI tools in pharmacology MCQs: Accuracy, sensitivity, and specificity.PloS one · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Large language models (LLMs), such as ChatGPT-4, Gemini, and Microsoft Copilot, have been instrumental in various domains, including healthcare, where they enhance health literacy and aid in patient decision-making. Given the complexities involved in breast imaging procedures, accurate and comprehensible information is vital for patient engagement and compliance. This study aims to evaluate the readability and accuracy of the information provided by three prominent LLMs, ChatGPT-4, Gemini, and Microsoft Copilot, in response to frequently asked questions in breast imaging, assessing their potential to improve patient understanding and facilitate healthcare communication. Methodology We collected the most common questions on breast imaging from clinical practice and posed them to LLMs. We then evaluated the responses in terms of readability and accuracy. Responses from LLMs were analyzed for readability using the Flesch Reading Ease and Flesch-Kincaid Grade Level tests and for accuracy through a radiologist-developed Likert-type scale. Results The study found significant variations among LLMs. Gemini and Microsoft Copilot scored higher on readability scales (p < 0.001), indicating their responses were easier to understand. In contrast, ChatGPT-4 demonstrated greater accuracy in its responses (p < 0.001). Conclusions While LLMs such as ChatGPT-4 show promise in providing accurate responses, readability issues may limit their utility in patient education. Conversely, Gemini and Microsoft Copilot, despite being less accurate, are more accessible to a broader patient audience. Ongoing adjustments and evaluations of these models are essential to ensure they meet the diverse needs of patients, emphasizing the need for continuous improvement and oversight in the deployment of artificial intelligence technologies in healthcare.
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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.