Evaluating Artificial Intelligence Chatbots for Patient Education on Thyroid Radiofrequency Ablation: An Analysis of Accuracy, Quality, and Readability
AI chatbot performance varied across platforms for thyroid RFA queries. Chatbots were generally reliable for straightforward factual information but were less dependable for judgment or context-dependent assessments. These AI tools should supplement, not replace, clinician-vetted patient education and institutional materials.
Authors
- Marika D. Russell (ORCID: https://orcid.org/0000-0001-5436-3649)
- Lisa Ann Orloff (ORCID: https://orcid.org/0000-0002-2424-227X)
- Daniel J. Campbell (ORCID: https://orcid.org/0000-0001-9001-5705)
- Julia E. Noel (ORCID: https://orcid.org/0000-0001-9955-3315)
- Jonathon O. Russell (ORCID: https://orcid.org/0000-0003-2903-9649)
- Elizabeth Cottrill
- Catherine F. Sinclair (ORCID: https://orcid.org/0000-0001-7129-7372)
- Vaninder K. Dhillon (ORCID: https://orcid.org/0000-0001-7551-3634)
- Jacob Beiriger (ORCID: https://orcid.org/0000-0001-8552-787X)
- Leonard E. Estephan (ORCID: https://orcid.org/0000-0001-9599-1510)
- David Goldenberg
Institutions
- Massachusetts Eye and Ear Infirmary (US)
- Pennsylvania State University (US)
- Johns Hopkins University (US)
- Thomas Jefferson University (US)
- Johns Hopkins Medicine (US)
- Sidney Kimmel Cancer Center (US)
- Jefferson University Hospitals (US)
- Diabetes & Endocrine Associates (US)
- Stanford Medicine (US)
- Monash University (AU)
- Penn State Milton S. Hershey Medical Center (US)
- Stanford University (US)
Publication Details
- Journal
- Thyroid
- Published
- 2026-01-13
- DOI
- https://doi.org/10.1177/10507256251414974
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00