一个基于人工智能的强化学习模型,用于皮肤癌的决策支持
Catarina Barata1, Veronica Rotemberg2, Noel C F Codella3
1Institute for Systems and Robotics, LARSyS, Instituto Superior Técnico, Lisbon, Portugal.
Nature medicine
|July 27, 2023
概括
人类的偏好可以增强诊断人工智能 (AI). 强化学习提高了皮肤癌检测灵敏度和皮肤科医生的准确性,这表明AI可以更好地支持临床决策.
科学领域:
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
- 机器学习 机器学习
背景情况:
- 诊断人工智能 (AI) 决策支持系统旨在提高临床准确性.
- 将人类偏好集成到人工智能算法中是提高诊断能力的未经探索的领域.
研究的目的:
- 研究将人类偏好纳入医疗诊断的AI决策支持中的潜力.
- 评估强化学习 (RL) 与人源奖励结构在改善皮肤癌检测诊断AI性能方面的有效性.
主要方法:
- 利用强化学习 (RL) 以非统一的奖励和处罚,基于专家生成的表格来平衡诊断错误.
- 对比RL模型与传统的监督学习 (SL) 进行皮肤癌诊断.
- 评估RL模型对皮肤科医生诊断准确性和管理决策的影响.
主要成果:
- RL显著改善了黑色素瘤 (61.4%至79.5%) 和基底细胞癌 (79.4%至87.1%) 的敏感性.
- 人工智能过度自信降低了,同时保持了准确性.
- 皮肤科医生正确诊断率增加了12.0%,最佳管理决策从57.4%提高到65.3%.
结论:
- 通过RL纳入人类偏好可以显著提高医疗成像中的诊断AI性能.
- 这种方法提高了诊断准确度,并支持医疗保健专业人员更好地做出临床决策.
- 研究结果表明,开发更有效和与人类一致的AI诊断工具是一个有希望的方向.
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