Explainable AI for Tracing Botanical Origin and Quality Markers of Traditional Herbal Medicines
Keywords:
explainable artificial intelligence; botanical origin tracing; quality markers; herbal medicines; systems architecture; metabolomics; governance; interpretability; robust machine learningAbstract
Traditional herbal medicines present distinctive scientific and regulatory challenges because their identity, therapeutic value, and safety depend on botanical origin, cultivation conditions, processing history, and the integrity of complex chemical profiles. Machine learning increasingly supports the authentication of herbal materials and the detection of quality markers, yet many high-performing models remain opaque to regulators, practitioners, and supply chain stakeholders. This paper develops a system-level perspective on explainable artificial intelligence for tracing botanical origin and quality markers in traditional herbal medicines. It examines how explanation methods can be integrated into the broader analytical pipeline, spanning spectral data acquisition, metabolomic profiling, feature engineering, classification, and decision support. Rather than treating explainability as a localized post hoc technique, the paper argues that robust explanation requires an architectural approach that coordinates data provenance, model modularity, uncertainty reporting, and interpretable feature representation. The discussion focuses on structural trade-offs between predictive performance and interpretability, the governance of distributed data infrastructure, the deployment of explainable systems across heterogeneous laboratories and regulatory environments, and the implications of fairness and robustness for public health. The paper also compares developments in medical artificial intelligence, chemometrics, and natural product authentication to identify transferable design principles. It emphasizes that sustainable deployment depends not only on algorithmic transparency but also on institutional mechanisms for validation, audit, and continuous monitoring. The analysis concludes with policy considerations and forward-looking perspectives on building accountable, interpretable, and scientifically credible artificial intelligence systems for herbal medicine quality assurance.
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