在糖尿病视网膜病变风险评估中ChatGPT的实用性:与临床诊断进行比较研究
Keerthana Raghu1, Tamilselvi S2, Chitralekha S Devishamani1
1Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India.
Clinical ophthalmology (Auckland, N.Z.)
|January 2, 2024
概括
聊天GPT显示了作为查糖尿病视网膜病变 (DR) 风险的初步工具的潜力. 虽然证明了出色的可靠性,但需要进一步优化,以便在DR预测中准确的临床应用.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 糖尿病视网膜病变 (DR) 是糖尿病患者视力丧失的主要原因.
- 早期发现和管理DR是预防严重视力损伤的关键.
- 人工智能模型为医疗保健中的新型诊断和查工具提供了潜力.
研究的目的:
- 评估AI模型ChatGPT在预测糖尿病视网膜病变 (DR) 风险方面的有效性.
主要方法:
- 一项回顾性观察研究分析了111名糖尿病患者的数据.
- 临床和生物化学数据,包括黄斑光学连贯性断层扫描 (OCT) 中央子场厚度 (CST),被输入到ChatGPT-4.
- 将ChatGPT的预测与眼科医生诊断的DR状态进行了比较.
主要成果:
- 聊天GPT在预测方面表现出极好的可靠性,其类内相关系数 (ICC) 值为0.936 (有CST) 和0.915 (没有CST).
- 灵敏度在67% (使用CST) 到73% (不使用CST) 之间,而特异性为68% (使用CST) 和54% (不使用CST).
- 科恩的卡帕仅表明了ChatGPT预测和临床DR诊断之间的公平一致 (卡帕=0.263与CST,0.351没有CST).
结论:
- 聊天GPT显示承诺作为糖尿病视网膜病变的初步查工具.
- 在DR风险评估得到广泛的临床采用之前,需要进一步的细化和优化.
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