人工智能可以促进对膀癌患者病例情景的风险分层算法的应用
Max S Yudovich1, Ahmad N Alzubaidi1, Jay D Raman1
1Penn State Health Milton S. Hershey Medical Center, Hershey, PA, USA.
Clinical Medicine Insights. Oncology
|November 19, 2024
概括
通过使用国家综合癌症网络 (NCCN) 的指导方针,ChatGPT,特别是GPT-4,可以准确地分层风险非肌肉侵入性膀癌 (NMIBC) 患者. 提供文本上下文显著提高了GPT-4的准确性,使其成为临床风险评估的潜在工具.
科学领域:
- 人工智能在医学中的应用
- 在瘤学瘤学.
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
背景情况:
- 国家综合癌症网络 (NCCN) 为非肌肉侵入性膀癌 (NMIBC) 风险分层提供了指导方针.
- 这些指南根据临床和疾病特征将患者分为低风险,中风险和高风险组.
- 以前的研究表明,ChatGPT能够预测结肠癌查间隔.
研究的目的:
- 评估ChatGPT在应用NCCNNNMIBC风险分层指南方面的能力.
- 评估不同语境信息格式 (文本与图像) 对ChatGPT性能的影响.
- 为了比较GPT-3.5和GPT-4在这个任务中的性能.
主要方法:
- 开发了36个假设的NMIBC患者场景.
- GPT-3.5和GPT-4被测试了风险分层与或没有NCCN指南的背景 (文本和图像).
- 绩效是根据所提供的场景的准确风险分层来评估的.
主要成果:
- 当提供NCCN文本指南时,GPT-4在风险分层NMIBC患者中实现了100%的准确性,这与GPT-3.5.5相比是显著的改善.
- 在没有上下文的情况下,GPT-4的准确率为83%,在图像上下文的情况下为81%.
- 两种模型都在与中等风险的NMIBC分层斗争,错误的分层往往高估了风险.
结论:
- 当与NCCN指南上下文一起提供时,GPT-4在NMIBC风险分层中显示出高准确性.
- 对于中等风险的NMIBC分层仍然是一个挑战,并倾向于高估风险.
- GPT-4具有作为NMIBC风险分层的临床工具的潜力,等待进一步验证.
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