Evidence map›Paper›PMID 41377140›Full record

ReviewJournal of pharmaceutical analysis2025

Artificial intelligence guided Raman spectroscopy in biomedicine: Applications and prospects.

Yuan Liu, Sitong Chen, Xiaomin Xiong, Zhenguo Wen, Long Zhao, Bo Xu, Qianjin Guo, Jianye Xia, Jianfeng Pei

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of pharmaceutical analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yuan LiuDepartment of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, 102627, China.
Sitong ChenDepartment of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, 102627, China.
Xiaomin XiongChongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital and School of Medicine, Chongqing University, Chongqing, 400000, China.
Zhenguo WenDepartment of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, 102627, China.
Long ZhaoDepartment of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, 102627, China.
Bo XuChongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital and School of Medicine, Chongqing University, Chongqing, 400000, China.
Qianjin GuoAcademy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102627, China.
Jianye XiaTianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300074, China.
Jianfeng PeiCenter for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial intelligenceBiomedicineBioprocessDeep learningProcess analytical technologyRaman spectrum

Identifiers

PMID41377140
PMCPMC12688681

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.