ArticleRadiology. Imaging cancer2024
Evaluating the Use of ChatGPT to Accurately Simplify Patient-centered Information about Breast Cancer Prevention and Screening.
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Article in Radiology. Imaging cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23citing papers in PubMed
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23 citing papers in PubMed, 45 citations in OpenAlex.
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- The Use of ChatGPT-4.0 to Simplify Breast Pathology Reports: A Study on Readability and Accuracy.Annals of surgical oncology · 2025Article
- Best Practices for the Safe Use of Large Language Models and Other Generative AI in Radiology.Radiology · 2025Review
- Evaluating the Quality and Understandability of Radiology Report Summaries Generated by ChatGPT: Survey Study.JMIR formative research · 2025Article
- Assessing the Accuracy and Readability of Large Language Model Guidance for Patients on Breast Cancer Surgery Preparation and Recovery.Journal of clinical medicine · 2025Article
- Bridging knowledge gaps in breast cancer prevention: Insights from Ethiopia.World journal of clinical oncology · 2025Article
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- 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
- Precision Oncology in Non-small Cell Lung Cancer: A Comparative Study of Contextualized ChatGPT Models.Cureus · 2025Article
- Enhancing Patient Comprehension of Glomerular Disease Treatments Using ChatGPT.Healthcare (Basel, Switzerland) · 2024Article
- To trust or not to trust: evaluating the reliability and safety of AI responses to laryngeal cancer queries.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- [Large Language Models: A Comprehensive Guide for Radiologists].Journal of the Korean Society of Radiology · 2024Review
- Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care.European journal of investigation in health, psychology and education · 2024Article
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Authors and funding
7 authors at 1 institution in 1 country.
Hana L HaverFrom the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, First Floor, Rm 1172, Baltimore, MD 21201 (H.L.H., A.K.G., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. James Clark School of Engineering, University of Maryland-College Park, College Park, Md (P.H.Y.).ORCID 0000-0002-8382-4077
Anuj K GuptaFrom the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, First Floor, Rm 1172, Baltimore, MD 21201 (H.L.H., A.K.G., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. James Clark School of Engineering, University of Maryland-College Park, College Park, Md (P.H.Y.).
Emily B AmbinderFrom the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, First Floor, Rm 1172, Baltimore, MD 21201 (H.L.H., A.K.G., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. James Clark School of Engineering, University of Maryland-College Park, College Park, Md (P.H.Y.).ORCID 0000-0001-6282-5858
Manisha BahlFrom the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, First Floor, Rm 1172, Baltimore, MD 21201 (H.L.H., A.K.G., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. James Clark School of Engineering, University of Maryland-College Park, College Park, Md (P.H.Y.).ORCID 0000-0002-7728-7690
Eniola T OluyemiFrom the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, First Floor, Rm 1172, Baltimore, MD 21201 (H.L.H., A.K.G., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. James Clark School of Engineering, University of Maryland-College Park, College Park, Md (P.H.Y.).ORCID 0000-0001-8956-4379
Jean JeudyFrom the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, First Floor, Rm 1172, Baltimore, MD 21201 (H.L.H., A.K.G., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. James Clark School of Engineering, University of Maryland-College Park, College Park, Md (P.H.Y.).ORCID 0000-0002-6028-459X
Paul H YiFrom the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, First Floor, Rm 1172, Baltimore, MD 21201 (H.L.H., A.K.G., J.J., P.H.Y.); The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (E.B.A., E.T.O.); Department of Radiology, Division of Breast Imaging, Massachusetts General Hospital, Boston, Mass (M.B.); Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (P.H.Y.); and Fischell Department of Bioengineering, A. James Clark School of Engineering, University of Maryland-College Park, College Park, Md (P.H.Y.).ORCID 0000-0001-9433-8093
University of Maryland, Baltimore · US
Funding
Development and Clinical Implementation of an Artificial Intelligence Tool to Predict Risk of Upgrade of Ductal Carcinoma In SituK08CA241365 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI BAHL, MANISHA · 2019 to 2023
$1.3MNCI NIH HHS K08 CA241365
Abstract
Purpose To evaluate the use of ChatGPT as a tool to simplify answers to common questions about breast cancer prevention and screening. Materials and Methods In this retrospective, exploratory study, ChatGPT was requested to simplify responses to 25 questions about breast cancer to a sixth-grade reading level in March and August 2023. Simplified responses were evaluated for clinical appropriateness. All original and simplified responses were assessed for reading ease on the Flesch Reading Ease Index and for readability on five scales: Flesch-Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, Automated Readability Index, and the Simple Measure of Gobbledygook (ie, SMOG) Index. Mean reading ease, readability, and word count were compared between original and simplified responses using paired
Indexed as
Breast NeoplasmsHealth LiteracyEarly Detection of CancerFemaleHumansPatient-Centered CareRetrospective StudiesBreastEducationHealth Policy and PracticeInformaticsMammographyOncologyScreeningTechnology Assessment
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