Guirguis Christopher A, Crossley Jason R, Malekzadeh Sonya
Otolaryngology - Head and Neck Surgery, MedStar Georgetown University Hospital, Washington, D.C., USA.
Cureus. 2023 Apr 10;15(4):e37368. doi: 10.7759/cureus.37368. eCollection 2023 Apr.
This ChatGPT-driven case report describes a unique presentation of neurosarcoidosis. The patient, a 58-year-old female, initially presented with hoarseness and was found to have bilateral jugular foramen tumors and thoracic lymphadenopathy. Imaging revealed significant enlargement and thickening of the vagus nerve and a separate mass of the cervical sympathetic trunk. The patient was referred for an ultrasound-guided biopsy of the abnormal neck masses to establish a pathologic diagnosis. The patient subsequently underwent neck dissection for exposure of the vagus nerve and isolation of the great vessels in preparation for a transmastoid approach to the skull base. The presence of multifocal tumors prompted the need for a biopsy, which ultimately revealed sarcoid granulomas in the nervous system. The patient was diagnosed with neurosarcoidosis. This case highlights the potential for sarcoidosis to affect the nervous system, with multiple cranial nerve involvement, seizures, and cognitive impairment. It also emphasizes the need for a combination of clinical, radiological, and pathological findings for an accurate diagnosis of neurosarcoidosis. Additionally, this case highlights the utility of natural language processing (NLP), as the entire case report was written using ChatGPT. This report serves as a comparison of the quality of case reports generated by humans versus NLP algorithms. The original case report can be found in the references.
这份由ChatGPT生成的病例报告描述了神经结节病的一种独特表现。患者为一名58岁女性，最初表现为声音嘶哑，检查发现双侧颈静脉孔肿瘤及胸部淋巴结病。影像学检查显示迷走神经显著增粗，颈交感干有一独立肿块。患者被转诊接受颈部异常肿块的超声引导下活检以明确病理诊断。患者随后接受颈部清扫术，以暴露迷走神经并分离大血管，为经乳突入路至颅底做准备。多灶性肿瘤的存在促使需要进行活检，最终在神经系统中发现了结节病肉芽肿。该患者被诊断为神经结节病。本病例突出了结节病累及神经系统的可能性，可出现多组颅神经受累、癫痫发作及认知障碍。它还强调了综合临床、影像学和病理检查结果以准确诊断神经结节病的必要性。此外，本病例突出了自然语言处理（NLP）的效用，因为整个病例报告是使用ChatGPT撰写的。本报告旨在比较人类生成的病例报告与NLP算法生成的病例报告的质量。原始病例报告见参考文献。
