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English(EN) Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability

综述论文详述数字健康领域的可信赖人工智能

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新近发表在arXiv上的综述论文综合了可信赖人工智能领域的最新进展，特别关注数字健康领域的鲁棒性和可解释性。论文概述了公平性、问责制和隐私等关键维度，这些对于机器学习在医疗保健中的伦理整合至关重要。文章探讨了重症监护和代谢健康等领域的特定应用信任考量，并详细介绍了改进模型鲁棒性和可解释性的方法，包括针对数据稀疏性和分布偏移的技术。 AI

影响 为在关键医疗保健应用中开发更可靠、更具可解释性的人工智能解决方案提供了框架。

排序理由 该条目是一篇发表在arXiv上的综合性综述论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

综述论文详述数字健康领域的可信赖人工智能

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该条目是一篇发表在arXiv上的综合性综述论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Abdullah Mamun, Shovito Barua Soumma, Hassan Ghasemzadeh ·

    数字健康领域的可信赖人工智能：鲁棒性与可解释性的综合评述

    arXiv:2608.02238v1 Announce Type: cross Abstract: Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital health. Key dimensions, including robustness, explainability, fairness, accountabi…

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