在人工智能产生的医学知识中分析和消除幻觉的策略
Fengxian Chen1, Yan Li1, Yaolong Chen2
1School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
Journal of evidence-based medicine
|September 22, 2025
概括
医疗保健中的生成人工智能 (GAI) 可以产生幻觉,产生错误的信息,从而危及患者的安全. 本研究详细介绍了知识图和专家反等策略,以提高AI可靠性和临床使用的准确性.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 人工智能 (AI) 在医疗诊断和治疗方面表现有前途.
- 生成型人工智能 (GAI) 模型由于"幻觉" - - 事实上不正确的输出而危及患者的安全.
- 解决GAI幻觉对于可靠的临床决策至关重要.
研究的目的:
- 系统地分析医疗保健中GAI幻觉的原因.
- 为减轻这些幻觉提出多维策略.
- 提高AI在临床环境中的可信度和以患者为中心.
主要方法:
- 在数据,培训和推断中分析幻觉的原因.
- 使用知识图和多阶段培训进行技术优化.
- 通过专家反循环和多学科工作流程进行临床整合.
- 实施强大的评估系统,包括对抗性测试和现实世界的验证.
主要成果:
- 技术优化显著减少了GAI幻觉.
- 临床整合提高了人工智能输出的可靠性.
- 强大的评估系统在临床环境中提高了事实准确性.
- 技术进步与临床治理的协调是关键.
结论:
- 多维策略有效地减轻医疗保健中的GAI幻觉.
- 知识图,多阶段培训和专家反对于可靠的AI至关重要.
- 强有力的评估和临床治理对于值得信赖,以患者为中心的AI至关重要.
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