Artificial intelligence guided Raman spectroscopy in biomedicine: Applications and prospects.
10.1016/j.jpha.2025.101271
- Author:
Yuan LIU
1
;
Sitong CHEN
1
;
Xiaomin XIONG
2
;
Zhenguo WEN
1
;
Long ZHAO
1
;
Bo XU
2
;
Qianjin GUO
3
;
Jianye XIA
4
;
Jianfeng PEI
5
Author Information
1. Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, 102627, China.
2. Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital and School of Medicine, Chongqing University, Chongqing, 400000, China.
3. Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102627, China.
4. Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300074, China.
5. Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.
- Publication Type:Review
- Keywords:
Artificial intelligence;
Biomedicine;
Bioprocess;
Deep learning;
Process analytical technology;
Raman spectrum
- From:
Journal of Pharmaceutical Analysis
2025;15(11):101271-101271
- CountryChina
- Language:English
-
Abstract:
Due to its high sensitivity and non-destructive nature, Raman spectroscopy has become an essential analytical tool in biopharmaceutical analysis and drug development. Despite of the computational demands, data requirements, or ethical considerations, artificial intelligence (AI) and particularly deep learning algorithms has further advanced Raman spectroscopy by enhancing data processing, feature extraction, and model optimization, which not only improves the accuracy and efficiency of Raman spectroscopy detection, but also greatly expands its range of application. AI-guided Raman spectroscopy has numerous applications in biomedicine, including characterizing drug structures, analyzing drug forms, controlling drug quality, identifying components, and studying drug-biomolecule interactions. AI-guided Raman spectroscopy has also revolutionized biomedical research and clinical diagnostics, particularly in disease early diagnosis and treatment optimization. Therefore, AI methods are crucial to advancing Raman spectroscopy in biopharmaceutical research and clinical diagnostics, offering new perspectives and tools for disease treatment and pharmaceutical process control. In summary, integrating AI and Raman spectroscopy in biomedicine has significantly improved analytical capabilities, offering innovative approaches for research and clinical applications.
