{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T15:32:45Z","timestamp":1793287965700,"version":"4.5.0"},"reference-count":26,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Thyroid\u00ae"],"published-print":{"date-parts":[[2026,2,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) chatbots are increasingly being used by patients to obtain medical information. Comparison between platforms with specialty-specific physician assessment remains limited. This study compares the quality, factual accuracy, readability, and consistency of responses generated by four publicly available AI chatbots when answering patient-centered questions about thyroid radiofrequency ablation (RFA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      We conducted a cross-sectional analysis of chatbot-generated responses using 20 standardized clinical questions about thyroid RFA. Responses from ChatGPT-4, Gemini, Copilot, and Perplexity were evaluated by six blinded physician reviewers experienced in thyroid RFA using 5-point Likert scales for global quality and factual accuracy. Higher Likert scale scores indicated better performance. Readability and response length were analyzed with established metrics. Statistical significance was defined as\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      &lt; 0.05.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Gemini achieved the highest mean scores for global quality (4.08 \u00b1 0.87) and accuracy (3.76 \u00b1 1.05), with significantly better performance than ChatGPT and Copilot (\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      &lt; 0.005). ChatGPT responses were significantly longer and more readable. Score variability across questions was lowest for Gemini. Copilot and Perplexity ranked lowest across most domains. Question-level analysis identified specific prompts that best discriminated between platforms.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10507256251414974","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T14:25:31Z","timestamp":1768314331000},"page":"162-168","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Evaluating Artificial Intelligence Chatbots for Patient Education on Thyroid Radiofrequency Ablation: An Analysis of Accuracy, Quality, and Readability"],"prefix":"10.1177","volume":"36","author":[{"given":"Jacob","family":"Beiriger","sequence":"first","affiliation":[{"name":"Sidney Kimmel Medical College, Thomas Jefferson University Hospitals, Philadelphia, Pennsylvania, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonard E.","family":"Estephan","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology \u2013 Head and Neck Surgery, Thomas Jefferson University Hospitals, Philadelphia, Pennsylvania, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Campbell","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology \u2013 Head and Neck Surgery, Thomas Jefferson University Hospitals, Philadelphia, Pennsylvania, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vaninder Kaur","family":"Dhillon","sequence":"additional","affiliation":[{"name":"Division of Laryngology, Department of Head and Neck Surgery, Johns Hopkins University, Bethesda, Maryland, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Goldenberg","sequence":"additional","affiliation":[{"name":"Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, The Pennsylvania State University-Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julia","family":"Noel","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology - Head &amp; Neck Surgery, Stanford University School of Medicine, Stanford, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa A.","family":"Orloff","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology - Head &amp; Neck Surgery, Stanford University School of Medicine, Stanford, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marika","family":"Russell","sequence":"additional","affiliation":[{"name":"Division of Thyroid and Parathyroid Endocrine Surgery, Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathon O.","family":"Russell","sequence":"additional","affiliation":[{"name":"Russell Center for Endocrine Health, Columbia, Maryland, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine F.","family":"Sinclair","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology Head and Neck Surgery, Mount Sinai West Hospital, New York, New York, USA."},{"name":"Department of Surgery, Monash University, Melbourne, Victoria, Australia."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth E.","family":"Cottrill","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology \u2013 Head and Neck Surgery, Thomas Jefferson University Hospitals, Philadelphia, Pennsylvania, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"AP-NORC Center for Public Affairs Research. Young adults are leading the way in AI adoption. AP-NORC at the University of Chicago; 2025. Available from: https:\/\/apnorc.org\/projects\/young-adults-leading-the-way-in-ai-adoption\/ [Last accessed: October29 2025]."},{"key":"e_1_3_3_3_2","unstructured":"Brown D Dapena K Stern J. The great AI challenge: We test five top bots on useful everyday skills. The Wall Street Journal; May 25 2024. Available from: https:\/\/www.wsj.com\/tech\/personal-tech\/ai-chatbots-chatgpt-gemini-copilot-perplexity-claude-9b4002e"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2023.1838"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.2196\/69955"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.59898"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.japh.2024.102110"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2023.0491"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1210\/clinem\/dgae235"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12325-025-03198-4"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/curroncol31090389"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1002\/ohn.489"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1037\/h0057532"},{"key":"e_1_3_3_14_2","doi-asserted-by":"crossref","unstructured":"Kincaid J. Peter ; Fishburne Robert P.Jr. ; Rogers Richard L. ; and Chissom Brad S. \u201cDerivation Of New Readability Formulas (Automated Readability Index Fog Count And Flesch Reading Ease Formula) For Navy Enlisted Personnel\u201d (1975). Institute for Simulation and Training 56. 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