{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:43:31Z","timestamp":1791895411167,"version":"4.3.4"},"reference-count":28,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Cardio"],"abstract":"\n Background<\/jats:title>\n Amyloidosis, a rare multisystem condition, often requires complex, multidisciplinary care. Its low prevalence underscores the importance of efforts to ensure the availability of high-quality patient education materials for better outcomes. ChatGPT (OpenAI) is a large language model powered by artificial intelligence that offers a potential avenue for disseminating accurate, reliable, and accessible educational resources for both patients and providers. Its user-friendly interface, engaging conversational responses, and the capability for users to ask follow-up questions make it a promising future tool in delivering accurate and tailored information to patients.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n We performed a multidisciplinary assessment of the accuracy, reproducibility, and readability of ChatGPT in answering questions related to amyloidosis.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In total, 98 amyloidosis questions related to cardiology, gastroenterology, and neurology were curated from medical societies, institutions, and amyloidosis Facebook support groups and inputted into ChatGPT-3.5 and ChatGPT-4. Cardiology- and gastroenterology-related responses were independently graded by a board-certified cardiologist and gastroenterologist, respectively, who specialize in amyloidosis. These 2 reviewers (RG and DCK) also graded general questions for which disagreements were resolved with discussion. Neurology-related responses were graded by a board-certified neurologist (AAH) who specializes in amyloidosis. Reviewers used the following grading scale: (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, and (4) completely incorrect. Questions were stratified by categories for further analysis. Reproducibility was assessed by inputting each question twice into each model. The readability of ChatGPT-4 responses was also evaluated using the Textstat library in Python (Python Software Foundation) and the Textstat readability package in R software (R Foundation for Statistical Computing).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n ChatGPT-4 (n=98) provided 93 (95%) responses with accurate information, and 82 (84%) were comprehensive. ChatGPT-3.5 (n=83) provided 74 (89%) responses with accurate information, and 66 (79%) were comprehensive. When examined by question category, ChatGTP-4 and ChatGPT-3.5 provided 53 (95%) and 48 (86%) comprehensive responses, respectively, to \u201cgeneral questions\u201d (n=56). When examined by subject, ChatGPT-4 and ChatGPT-3.5 performed best in response to cardiology questions (n=12) with both models producing 10 (83%) comprehensive responses. For gastroenterology (n=15), ChatGPT-4 received comprehensive grades for 9 (60%) responses, and ChatGPT-3.5 provided 8 (53%) responses. Overall, 96 of 98 (98%) responses for ChatGPT-4 and 73 of 83 (88%) for ChatGPT-3.5 were reproducible. The readability of ChatGPT-4\u2019s responses ranged from 10th to beyond graduate US grade levels with an average of 15.5 (SD 1.9).<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Large language models are a promising tool for accurate and reliable health information for patients living with amyloidosis. However, ChatGPT\u2019s responses exceeded the American Medical Association\u2019s recommended fifth- to sixth-grade reading level. Future studies focusing on improving response accuracy and readability are warranted. Prior to widespread implementation, the technology\u2019s limitations and ethical implications must be further explored to ensure patient safety and equitable implementation.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/53421","type":"journal-article","created":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T13:00:40Z","timestamp":1713531640000},"page":"e53421","source":"Crossref","is-referenced-by-count":21,"title":["A Multidisciplinary Assessment of ChatGPT\u2019s Knowledge of Amyloidosis: Observational Study"],"prefix":"10.2196","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3479-0365","authenticated-orcid":false,"given":"Ryan C","family":"King","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6191-2631","authenticated-orcid":false,"given":"Jamil S","family":"Samaan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2703-5954","authenticated-orcid":false,"given":"Yee Hui","family":"Yeo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2737-5394","authenticated-orcid":false,"given":"Yuxin","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7319-8677","authenticated-orcid":false,"given":"David C","family":"Kunkel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3432-9747","authenticated-orcid":false,"given":"Ali A","family":"Habib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2588-2631","authenticated-orcid":false,"given":"Roxana","family":"Ghashghaei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2024,4,19]]},"reference":[{"key":"ref1","first-page":"19","volume":"19","author":"Papingiotis, G","year":"2021","journal-title":"E-Journal Cardiol Pract"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10741-021-10162-1"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1002\/ehf2.13652"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2022.11.022"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph20020968"},{"key":"ref6","unstructured":"Talking points about health literacyCenters for Disease Control and Prevention20212024-03-01https:\/\/www.cdc.gov\/healthliteracy\/shareinteract\/TellOthers.html#"},{"key":"ref7","unstructured":"ChatGPT: optimizing language models for dialogueOpenAI20232023-02-18https:\/\/online-chatgpt.com"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2023.1044"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3350\/cmh.2023.0089"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s11695-023-06603-5"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2023.1838"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1101\/2023.05.04.23289482"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1101\/2023.04.20.23288859"},{"key":"ref14","journal-title":"ArXiv"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.resuscitation.2023.110106"},{"key":"ref16","unstructured":"WeisBDHealth literacy: a manual for cliniciansAmerican Medical Association Foundation and American Medical Association20032024-03-01http:\/\/lib.ncfh.org\/pdfs\/6617.pdf"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-50884-w"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.oret.2023.05.022"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1093\/eurjcn\/zvad038"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajg.2023.08.001"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3390\/clinpract13060130"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfo.2023.05.006"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2021.16680"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(23)00225-X"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1101\/2023.08.28.23294730"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00939-z"},{"key":"ref27","unstructured":"New ways to manage your data in ChatGPTOpenAI2023-08-15https:\/\/openai.com\/blog\/new-ways-to-manage-your-data-in-chatgpt"},{"key":"ref28","unstructured":"Proposed regulatory framework for modifications to artificial intelligence\/machine learning (AI\/ML)-based software as a medical device (SaMD)US Food and Drug Administration20192024-02-28https:\/\/www.fda.gov\/files\/medical%20devices\/published\/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf"}],"container-title":["JMIR Cardio"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cardio.jmir.org\/2024\/1\/e53421","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:39:07Z","timestamp":1782841147000},"score":1,"resource":{"primary":{"URL":"https:\/\/cardio.jmir.org\/2024\/1\/e53421"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,19]]},"references-count":28,"URL":"https:\/\/doi.org\/10.2196\/53421","relation":{},"ISSN":["2561-1011"],"issn-type":[{"value":"2561-1011","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,19]]}}}