通过神经常微分方程（NODE）增强医疗预后的人机合作
Enhancing Human-Machine Teaming for Medical Prognosis Through Neural Ordinary Differential Equations (NODEs)
摘要：
机器学习（ML）最近被证明在包括医学在内的各种领域的预测和检测任务中可以与专家级的人类准确性相媲美。然而，尽管有这些令人印象深刻的发现，但在医学领域充分实现ML潜力的一个关键障碍是技术接受度。最近在产生可解释人工智能（XAI）方面的努力在提高一些ML模型的可解释性方面取得了进展，但这些努力受到其设计固有的限制：它们最擅长识别系统失败的原因，但在解释何时以及为什么模型的预测是正确的方面做得很差。我们认为，ML预测在专家领域的可接受性受到两个关键因素的限制：机器的预测范围超出了人类的能力，以及机器预测无法将人类的直觉纳入其模型。我们建议使用一种新的机器学习架构，神经常微分方程（NODE），以增强人类的理解和鼓励可接受性。我们的方法优先考虑人类的认知直觉在算法设计的中心，并提供了一个分布的预测，而不是单一的输出。我们解释了这种方法如何可以显着提高人机协作的预测任务在专家领域，如医疗保健。我们提出了一个模型，并通过从文献中扩展一个具体的例子来展示我们的模型如何推进未来混合人类-人工智能系统的愿景。
英文摘要：
Machine Learning (ML) has recently been demonstrated to rival expert-level human accuracy in prediction and detection tasks in a variety of domains, including medicine. Despite these impressive findings, however, a key barrier to the full realization of ML's potential in medical prognoses is technology acceptance. Recent efforts to produce explainable AI (XAI) have made progress in improving the interpretability of some ML models, but these efforts suffer from limitations intrinsic to their design: they work best at identifying why a system fails, but do poorly at explaining when and why a model's prediction is correct. We posit that the acceptability of ML predictions in expert domains is limited by two key factors: the machine's horizon of prediction that extends beyond human capability, and the inability for machine predictions to incorporate human intuition into their models. We propose the use of a novel ML architecture, Neural Ordinary Differential Equations (NODEs) to enhance human understanding and encourage acceptability. Our approach prioritizes human cognitive intuition at the center of the algorithm design, and offers a distribution of predictions rather than single outputs. We explain how this approach may significantly improve human-machine collaboration in prediction tasks in expert domains such as medical prognoses. We propose a model and demonstrate, by expanding a concrete example from the literature, how our model advances the vision of future hybrid Human-AI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
