Contreras Jhonatan, Bocklitz Thomas
Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743, Jena, Germany.
Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz, Centre for Photonics in Infection Research (LPI), Albert Einstein Straße 9, 07745, Jena, Germany.
Pflugers Arch. 2025 Apr;477(4):603-615. doi: 10.1007/s00424-024-02997-y. Epub 2024 Aug 1.
Explainable artificial intelligence (XAI) has gained significant attention in various domains, including natural and medical image analysis. However, its application in spectroscopy remains relatively unexplored. This systematic review aims to fill this gap by providing a comprehensive overview of the current landscape of XAI in spectroscopy and identifying potential benefits and challenges associated with its implementation. Following the PRISMA guideline 2020, we conducted a systematic search across major journal databases, resulting in 259 initial search results. After removing duplicates and applying inclusion and exclusion criteria, 21 scientific studies were included in this review. Notably, most of the studies focused on using XAI methods for spectral data analysis, emphasizing identifying significant spectral bands rather than specific intensity peaks. Among the most utilized AI techniques were SHapley Additive exPlanations (SHAP), masking methods inspired by Local Interpretable Model-agnostic Explanations (LIME), and Class Activation Mapping (CAM). These methods were favored due to their model-agnostic nature and ease of use, enabling interpretable explanations without modifying the original models. Future research should propose new methods and explore the adaptation of other XAI employed in other domains to better suit the unique characteristics of spectroscopic data.
可解释人工智能（XAI）在包括自然和医学图像分析在内的各个领域都受到了广泛关注。然而，其在光谱学中的应用仍相对未被探索。本系统综述旨在通过全面概述光谱学中XAI的当前状况，并识别与其实施相关的潜在益处和挑战来填补这一空白。遵循2020年PRISMA指南，我们在主要期刊数据库中进行了系统检索，共获得259条初始检索结果。在去除重复项并应用纳入和排除标准后，本综述纳入了21项科学研究。值得注意的是，大多数研究集中于使用XAI方法进行光谱数据分析，重点是识别重要的光谱带而非特定的强度峰值。最常用的人工智能技术包括SHapley Additive exPlanations（SHAP）、受局部可解释模型无关解释（LIME）启发的掩码方法以及类激活映射（CAM）。这些方法因其与模型无关的性质和易用性而受到青睐，能够在不修改原始模型的情况下进行可解释的说明。未来的研究应提出新方法，并探索其他领域中使用的其他XAI的适应性，以更好地适应光谱数据的独特特征。
