可解释人工智能在人类健康监测革命中的作用

Abdullah Alharthi, Ahmed Alqurashi, Turki Alharbi, Mohammed Alammar, Nasser Aldosari, Houssem Bouchekara, Yusuf Shaaban, Mohammad Shoaib Shahriar, Abdulrahman Al Ayidh
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引用次数: 0

摘要

疾病机制的复杂性和患者症状的多变性给开发有效的诊断工具带来了巨大障碍。虽然机器学习在医学诊断方面取得了长足的进步,但其决策过程往往缺乏透明度,这可能会危及患者的治疗效果。这凸显了对可解释人工智能(XAI)的迫切需要,它不仅能提供更清晰的信息,而且有可能显著改善患者护理。在这篇文献综述中,我们详细分析了通过搜索各种数据库确定的 XAI 方法,重点关注帕金森病、中风、抑郁症、癌症、心脏病和阿尔茨海默病等慢性疾病。文献检索揭示了 9 种流行的 XAI 算法在医疗保健领域的应用,并强调了每种算法的优缺点。因此,文章最后对 XAI 在人类健康监测中的挑战和未来研究机会进行了批判性评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Role of Explainable AI in Revolutionizing Human Health Monitoring
The complex nature of disease mechanisms and the variability of patient symptoms present significant obstacles in developing effective diagnostic tools. Although machine learning has made considerable advances in medical diagnosis, its decision-making processes frequently lack transparency, which can jeopardize patient outcomes. This underscores the critical need for Explainable AI (XAI), which not only offers greater clarity but also has the potential to significantly improve patient care. In this literature review, we conduct a detailed analysis of analyzing XAI methods identified through searches across various databases, focusing on chronic conditions such as Parkinson's, stroke, depression, cancer, heart disease, and Alzheimer's disease. The literature search revealed the application of 9 trending XAI algorithms in the field of healthcare and highlighted the pros and cons of each of them. Thus, the article is concluded with a critical appraisal of the challenges and future research opportunities for XAI in human health monitoring.
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