{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T06:09:13Z","timestamp":1792130953716,"version":"4.4.0"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Background and objectives<\/jats:title><jats:p>We propose the use of GPT-4 to facilitate initial history-taking in neurology and other medical specialties. A large language model (LLM) could be utilized as a digital twin which could enhance queryable electronic medical record (EMR) systems and provide healthcare conversational agents (HCAs) to replace waiting-room questionnaires.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this observational pilot study, we presented verbatim history of present illness (HPI) narratives from published case reports of headache, stroke, and neurodegenerative diseases. Three standard GPT-4 models were designated Models <jats:italic>P<\/jats:italic>: patient digital twin; <jats:italic>N<\/jats:italic>: neurologist to query Model P; and <jats:italic>S<\/jats:italic>: supervisor to synthesize the N-P dialogue into a derived HPI and formulate the differential diagnosis. Given the random variability of GPT-4 output, each case was presented five separate times to check consistency and reliability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The study achieved an overall HPI content retrieval accuracy of 81%, with accuracies of 84% for headache, 82% for stroke, and 77% for neurodegenerative diseases. Retrieval accuracies for individual HPI components were as follows: 93% for chief complaints, 47% for associated symptoms and review of systems, 76% for relevant symptom details, and 94% for histories of past medical, surgical, allergies, social, and family factors. The ranking of case diagnoses in the differential diagnosis list averaged in the 89th percentile.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Our tripartite LLM model demonstrated accuracy in extracting essential information from published case reports. Further validation with EMR HPIs, and then with direct patient care will be needed to move toward adaptation of enhanced diagnostic digital twins that incorporate real-time data from health-monitoring devices and self-monitoring assessments.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2024.1496866","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T06:51:09Z","timestamp":1737096669000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Neurological history both twinned and queried by generative artificial intelligence"],"prefix":"10.3389","volume":"11","author":[{"given":"Jung-Hyun","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eunhee","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergio L.","family":"Angulo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert A.","family":"McDougal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William W.","family":"Lytton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1136\/jnnp-2013-306881","article-title":"Clinical neurology: why this still matters in the 21st century","volume":"86","author":"Nicholl","year":"2015","journal-title":"J Neurol Neurosurg Psychiatry"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"285","DOI":"10.33588\/rn.7712.2023249","article-title":"Neurophobia among resident physicians in the emergency service","volume":"77","author":"Salda\u00f1a-Inda","year":"2023","journal-title":"Rev Neurol"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1186\/s12911-022-01928-0","article-title":"Obtaining patients\u2019 medical history using a digital device prior to consultation in primary care: study protocol for a usability and validity study","volume":"22","author":"Albrink","year":"2022","journal-title":"BMC Med Inform Decis Mak"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"e36074","DOI":"10.2196\/36074","article-title":"Digital tools designed to obtain the history of present illness from patients: scoping review","volume":"24","author":"Berdahl","year":"2022","journal-title":"J Med Internet Res"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"23333928221080336","DOI":"10.1177\/23333928221080336","article-title":"Clinical use of an electronic pre-visit questionnaire soliciting patient visit goals and interim history: a retrospective comparison between safety-net and non-safety-net clinics","volume":"9","author":"Shucard","year":"2022","journal-title":"Health Serv Res Manag Epidemiol"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"e17158","DOI":"10.2196\/17158","article-title":"Conversational agents in health care: scoping review and conceptual analysis","volume":"22","author":"Tudor Car","year":"2020","journal-title":"J Med Internet Res"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"e43068","DOI":"10.2196\/43068","article-title":"A medical ethics framework for conversational artificial intelligence","volume":"25","author":"Fournier-Tombs","year":"2023","journal-title":"J Med Internet Res"},{"key":"ref8","author":"Tu","year":"2024"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"e200293","DOI":"10.1212\/CPJ.0000000000200293","article-title":"GPT-4 performance for neurologic localization","volume":"14","author":"Lee","year":"2024","journal-title":"Neurol Clin Pract"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"e200311","DOI":"10.1212\/CPJ.0000000000200311","article-title":"Exploring the potential of large language models in neurology, using neurologic localization as an example","volume":"14","author":"Chiang","year":"2024","journal-title":"Neurol Clin Pract"},{"key":"ref11","author":"Yao","year":"2023"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"e1437","DOI":"10.3109\/0142159X.2013.818634","article-title":"The objective structured clinical examination (OSCE): AMEE guide no. 81. 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