ChatGPT Assisting Diagnosis of Neuro-ophthalmology Diseases Based on Case Reports.
ChatGPT Assisting Diagnosis of Neuro-ophthalmology Diseases Based on Case Reports.
复制标题
ChatGPT 基于病例报告辅助诊断神经眼科疾病。
DOI：
10.1101/2023.09.13.23295508
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Yousefi,Siamak
中科院分区：
文献类型：
--
作者：
Madadi,Yeganeh;Delsoz,Mohammad;Lao,PriscillaA;Fong,JosephW;Hollingsworth,TJ;Kahook,MalikY;Yousefi,Siamak
背景：评估基于病例报告的大型语言模型（LLM）聊天生成预训练转换器（ChatGPT）辅助诊断神经眼科疾病的准确性。方法：我们从公开的在线数据库中选择了22例不同的神经眼科疾病病例报告。这些病例包括神经眼科专科医生常见的各种慢性和急性疾病。我们将每个病例作为新的提示插入ChatGPTs (GPT-3.5和GPT-4)，并要求最可能的诊断。然后，我们将准确的信息提供给2位神经眼科医生，并记录他们的诊断，然后比较两种版本的chatgpt的反应。结果：GPT-3.5和GPT-4和2位神经眼科医生分别在22例中有13例（59%）、18例（82%）、19例（86%）和19例（86%）正确。各诊断源之间的一致性如下：GPT-3.5和GPT-4, 13 (59%)；GPT-3.5和第一神经眼科医生12人（55%）；GPT-3.5和第二神经眼科医生12人（55%）；GPT-4和第一神经眼科医生17人（77%）；GPT-4和第二神经眼科医生16人（73%）；第一和第二神经眼科医生17人（77%）。结论：GPT-3.5和GPT-4对神经眼科疾病的诊断准确率分别为59%和82%。随着进一步的发展，GPT-4可能有潜力用于临床护理环境，以帮助临床医生提供快速，准确的神经眼科患者诊断。在缺乏亚专业训练的神经眼科医生的临床环境中，使用像ChatGPT这样的llm的适用性值得进一步研究。
Background:To evaluate the accuracy of Chat Generative Pre-Trained Transformer (ChatGPT), a large language model (LLM), to assist in diagnosing neuro-ophthalmic diseases based on case reports.Methods:We selected 22 different case reports of neuro-ophthalmic diseases from a publicly available online database. These cases included a wide range of chronic and acute diseases commonly seen by neuro-ophthalmic subspecialists. We inserted each case as a new prompt into ChatGPTs (GPT-3.5 and GPT-4) and asked for the most probable diagnosis. We then presented the exact information to 2 neuro-ophthalmologists and recorded their diagnoses, followed by comparing responses from both versions of ChatGPT.Results:GPT-3.5 and GPT-4 and the 2 neuro-ophthalmologists were correct in 13 (59%), 18 (82%), 19 (86%), and 19 (86%) out of 22 cases, respectively. The agreements between the various diagnostic sources were as follows: GPT-3.5 and GPT-4, 13 (59%); GPT-3.5 and the first neuro-ophthalmologist, 12 (55%); GPT-3.5 and the second neuro-ophthalmologist, 12 (55%); GPT-4 and the first neuro-ophthalmologist, 17 (77%); GPT-4 and the second neuro-ophthalmologist, 16 (73%); and first and second neuro-ophthalmologists 17 (77%).Conclusions:The accuracy of GPT-3.5 and GPT-4 in diagnosing patients with neuro-ophthalmic diseases was 59% and 82%, respectively. With further development, GPT-4 may have the potential to be used in clinical care settings to assist clinicians in providing quick, accurate diagnoses of patients in neuro-ophthalmology. The applicability of using LLMs like ChatGPT in clinical settings that lack access to subspeciality trained neuro-ophthalmologists deserves further research.