Evidence map›Paper›PMID 42358785›Full record

ReviewChemical & biomedical imaging2026

AI-Assisted Coherent Raman Scattering Microscopy for Clinical Translation.

Yue Yu, Yuheng Guo, Kuan Luo, Minbiao Ji

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Chemical & biomedical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Yue YuState Key Laboratory of Surface Physics and Department of Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Shanghai Key Laboratory of Metasurfaces for Light Manipulation, Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China.
Yuheng GuoState Key Laboratory of Surface Physics and Department of Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Shanghai Key Laboratory of Metasurfaces for Light Manipulation, Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China.
Kuan LuoState Key Laboratory of Surface Physics and Department of Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Shanghai Key Laboratory of Metasurfaces for Light Manipulation, Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China.ORCID https://orcid.org/0009-0001-2661-1577
Minbiao JiState Key Laboratory of Surface Physics and Department of Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Shanghai Key Laboratory of Metasurfaces for Light Manipulation, Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China.ORCID https://orcid.org/0000-0002-9066-4008

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coherent Raman scattering (CRS) microscopy has emerged as a powerful bioimaging tool, combining bond-specific contrast with high sensitivity, to enable rapid histopathology and metabolic analysis. Yet, clinical translation demands further breakthroughs in acquisition speed, spatial/spectral resolution, imaging depth, and data interpretation. While hardware advances address some constraints, artificial intelligence (AI) now propels the field forward by decoding biological complexity beyond traditional limits. This review summarizes representative clinical applications of CRS microscopy, highlighting how AI-driven innovations are bridging technological gaps and accelerating deployment in medical diagnostics and research.

Indexed as

artificial intelligenceclinical diagnosiscoherent anti-Stokes Raman scattering microscopycoherent Raman scatteringdeep learninglabel-free histopathologystimulated Raman scattering microscopyvirtual staining

Identifiers

PMID42358785
PMCPMC13291945

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.