{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:04:07Z","timestamp":1782846247052,"version":"3.54.5"},"reference-count":23,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Large language models offer new possibilities for transforming unstructured clinical text into structured datasets. However, their performance in specialized and complex documentation environments, such as neurosurgery, remains insufficiently characterized. GPT-4o is a large language model with enhanced natural language capabilities, but its accuracy in extracting structured data from neurosurgical reports has not been systematically assessed.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This proof-of-concept study evaluated the feasibility and accuracy of GPT-4o for extracting predefined structured variables from unstructured neurosurgical reports of patients with vestibular schwannoma. Specific aims were to measure accuracy across variable types, assess the impact of prompt refinement, and explore the model\u2019s potential utility for research-oriented data mining.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In this retrospective single-center study, 10 consecutive patients with histologically confirmed vestibular schwannoma who underwent surgery between August and December 2023 were included. Four anonymized German-language documents per patient (discharge, surgical, histopathology, and 3-month follow-up reports) were processed using GPT-4o. Seventeen variables were extracted using a standardized zero-shot prompt. Targeted prompt refinements were subsequently applied for variables with low baseline accuracy. Two board-certified neurosurgeons independently validated all outputs, with discrepancies resolved by a senior neurosurgeon. Accuracy metrics, 95% CIs (Wilson method), and descriptive comparisons between variable types were calculated.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n GPT-4o achieved 100% accuracy for structured variables requiring minimal interpretation, including patient ID, date of birth, date of surgery, histopathological diagnosis, and World Health Organization grade. Several interpretative variables, such as symptoms at presentation, symptom type, symptom duration, extent of resection, and permanence of postoperative deficits, were also extracted with 100% accuracy. In contrast, intraoperative complications and new postoperative deficits were correctly identified in only 50% (5\/10) of cases using the zero-shot prompt. After targeted prompt refinement, accuracy for these variables improved substantially, reaching 90% to 100% in most cases. The mean accuracy was highest for structured categorical variables (97.5%, SD 4.6%), intermediate for binary variables (80%, SD 27.4%), and lowest for conditional text variables (66.7%, SD 28.9%), without statistically significant differences (\n P<\/jats:italic>\n =.25).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n GPT-4o demonstrated strong feasibility for structured data extraction from standardized neurosurgical reports, particularly for variables with limited semantic complexity. However, the high accuracy observed reflects a narrow and highly controlled context and should not be interpreted as evidence of general reliability across diverse clinical settings. Larger, multi-institutional, and multilingual studies are needed to determine broader applicability and potential clinical integration.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/77114","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T15:50:06Z","timestamp":1763394606000},"page":"e77114-e77114","source":"Crossref","is-referenced-by-count":0,"title":["Integrating GPT-4o Into Data Mining in Neurosurgery: Feasibility and Proof-of-Concept Study"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2112-2232","authenticated-orcid":false,"given":"Arthur Henrique","family":"Almeida Sales","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00fcrgen","family":"Beck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3330-3860","authenticated-orcid":false,"given":"J\u00fcrgen","family":"Grauvogel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"issue":"1","key":"R1","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","journal-title":"Nat Med"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","journal-title":"Sci Data"},{"issue":"6","key":"R3","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1093\/bib\/bbx044","article-title":"Deep learning for healthcare: review, opportunities and challenges","volume":"19","author":"Miotto","journal-title":"Brief Bioinform"},{"issue":"2","key":"R4","doi-asserted-by":"publisher","first-page":"94","DOI":"10.7861\/futurehosp.6-2-94","article-title":"The potential for artificial intelligence in healthcare","volume":"6","author":"Davenport","journal-title":"Future Healthc J"},{"issue":"e1","key":"R5","doi-asserted-by":"publisher","first-page":"e2","DOI":"10.1136\/amiajnl-2012-000969","article-title":"The coming age of data-driven medicine: translational bioinformatics\u2019 next frontier","volume":"19","author":"Shah","journal-title":"J Am Med Inform Assoc"},{"issue":"10","key":"R6","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","journal-title":"Nat Biomed Eng"},{"issue":"4","key":"R7","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","journal-title":"Bioinformatics"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2023.1180962","article-title":"Approach to machine learning for extraction of real-world data variables from electronic health records","volume":"14","author":"Adamson","journal-title":"Front Pharmacol"},{"issue":"13","key":"R9","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the future - big data, machine learning, and clinical medicine","volume":"375","author":"Obermeyer","journal-title":"N Engl J Med"},{"issue":"1","key":"R10","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00705-7","article-title":"Automated clinical coding: what, why, and where we are?","volume":"5","author":"Dong","journal-title":"NPJ Digit Med"},{"issue":"13","key":"R11","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big data and machine learning in health care","volume":"319","author":"Beam","journal-title":"JAMA"},{"key":"R12","doi-asserted-by":"publisher","DOI":"10.1007\/s40860-023-00204-w","article-title":"Secure data sharing with blockchain for remote health monitoring applications: a review","author":"Upadrista","journal-title":"J Reliab Intell Environ"},{"key":"R13","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Barredo Arrieta","journal-title":"Inf Fusion"},{"issue":"6","key":"R14","doi-asserted-by":"publisher","first-page":"e428","DOI":"10.1016\/S2589-7500(24)00061-X","article-title":"Ethical and regulatory challenges of large language models in medicine","volume":"6","author":"Ong","journal-title":"Lancet Digit Health"},{"key":"R15","doi-asserted-by":"crossref","unstructured":"Elbattah M Arnaud E Ghazali DA Dequen G . 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