REVIEW article
Front. Mol. Biosci.
Sec. Molecular Diagnostics and Therapeutics
Recent Advances in Applications of Artificial Intelligence-assisted Raman Spectroscopy in Diagnosis of Cancers
Provisionally accepted
- 1China-Japan Union Hospital, Jilin University, Changchun, China
- 2Shanxi Medical University, Taiyuan, China
- 3The Second Hospital of Jilin University, Changchun, China
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Cancer remains one of the leading causes of death worldwide. Among various diagnostic approaches, Raman spectroscopy (RS) has emerged as an advanced detection technology with the potential to distinguish cancerous tissues from normal ones. Notably, RS has been verified to show improved sensitivity, specificity, and accuracy for cancer diagnosis compared to conventional techniques. Recently, artificial intelligence (AI), developed to emulate human capabilities, has gained enough popularity and showcased its strength in learning high-level representations and recognizing complex patterns with remarkable efficiency. In this context, AI-assisted RS has been applied to the classification and prediction of cancer cells, achieving a higher accuracy of ~90% in correct predictions from a single spectrum. However, there has been no comprehensive review about the use of AI-assisted RS in distinguishing different types of cancer cells. Although AI-assisted RS has been widely utilized by researchers and clinicians over the past a few years to diagnose various cancers, including gastrointestinal, head and neck, cervical, and endocrine-related cancers, an in-depth investigation has yet to be conducted. This review aims to provide a narrative overview of the latest applications of AI-assisted RS in cancer diagnosis, summarize the key findings and benefits, discuss the associated challenges in different types of cancers, and present additional studies on AI-assisted RS in non-cancer diseases, such as fungal infections. Through this review, we hope to enhance researchers’ understanding of the potential value of AI-assisted RS in both cancer and non-cancer diseases, presenting a new diagnostic approach for clinical management, optimizing diagnostic efficacy, and ultimately improving patient survival outcomes.
Keywords: Raman spectroscopy, artificial intelligence, Cancers, Non-cancer diseases, diagnosis
Received: 21 Aug 2025; Accepted: 27 Nov 2025.
Copyright: © 2025 Zhu, Zhao, Zan, Ma and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jingxin Liu
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