可解释的人工智能和量子计算在精准医学中的联盟
Soumyadeep Ray1, Pronaya Bhattacharya2, Ebrahim A Mattar3
1Department of Biotechnology, Amity University, Newtown, Kolkata, 700135, West Bengal, India.
Computational and structural biotechnology journal
|December 4, 2025
概括
这项调查探讨了可解释的人工智能 (XAI) 和量子计算 (QC) 如何融合以推进精准医学. 它强调了用于药物发现和诊断的混合量子可解释的AI框架,旨在提供值得信赖的医疗保健.
科学领域:
- 量子计算是一种量子计算.
- 可解释的人工智能
- 精准医学是一门精准的医学.
背景情况:
- 先进的人工智能和量子计算的整合为个性化医疗保健提供了新的机会.
- 可解释的人工智能 (XAI) 对于理解医学中的复杂模型至关重要.
研究的目的:
- 调查2018-2025年XAI和QC在精密医学中的融合情况.
- 引入混合和量子可解释的方法的分类法.
- 识别障碍物和未来的研究方向.
主要方法:
- 复习量子算法,量子机器学习模型和XAI技术.
- 对NISQ硬件和编码约束的评估.
- 解释性方法的比较 (SHAP,LIME,QSHAP,QLRP,TSBA). 这里可以查看.
- 通过药物毒性预测和疾病爆发预测的案例研究进行演示.
主要成果:
- 混合变量量子管道的开发与XAI解释.
- 识别实际障碍,包括噪音,数据编码,监管和隐私.
- 临床量子优势和可扩展的QXAI框架的基准测试.
结论:
- XAI和QC的融合为可靠,可扩展的量子启用精密医疗保健提供了一条道路.
- 需要跨学科的努力来克服当前的局限性,并实现临床量子优势.
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