Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability.

中文翻译：

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基于张量网络机器学习的拉曼光谱数据肺癌筛查智能诊断方案

从学术研究到临床应用，人工智能 (AI) 对生物医学科学产生了巨大影响，例如生物标志物的检测和诊断、治疗的优化以及药物发现中新治疗靶点的识别。然而，当代人工智能技术，尤其是深度机器学习 (ML)，存在严重的不可解释性问题，可能无法控制地导致错误预测。可解释性对于 ML 的临床诊断尤为重要，因为消费者必须从坚实的基础或令人信服的解释中获得必要的安全感和信任感。在这项工作中，我们提出了一种张量网络 (TN)-ML 方法，通过筛选呼出气中挥发性有机化合物 (VOC) 的拉曼光谱数据来可靠地预测肺癌患者及其分期，普遍适合作为生物标志物，被认为是非侵入性肺癌筛查的理想方式。TN-ML 的预测基于映射到量子希尔伯特空间的呼吸样本的相互距离。由于量子概率解释，预测的确定性可以定量表征。高确定性样本的准确率几乎为 100$\%$。错误分类的样本表现出明显较低的确定性，因此可以被破译为异常，由人类专家处理以保证高可靠性。我们的工作揭示了将“生物医学人工智能”从传统的不可解释的 ML 方案转变为可解释的人类-ML 交互方法，以达到高精度和可靠性的目的。