ReviewJournal of materials chemistry. B2025
Chemical imaging for biological systems: techniques, AI-driven processing, and applications.
Review in Journal of materials chemistry. B, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Beyond structure: spectroscopic imaging in cryogenic electron microscopy.Current opinion in structural biology · 2026Review
- AI-Assisted Coherent Raman Scattering Microscopy for Clinical Translation.Chemical & biomedical imaging · 2026Review
- Advances in NIR-II Fluorescent Nanoprobes: Design Principles, Optical Engineering, and Emerging Translational Directions.Micromachines · 2025Review
- Next generation sequencing and beyond: a review of genomic sequencing methods.Functional & integrative genomics · 2025Article
- Approaches to modeling cancer metastasis: from bench to bedside.Frontiers in oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Visualizing the chemical compositions of biological samples is pivotal to advancing biological sciences, with the past two decades witnessing the emergence of innovative chemical imaging platforms such as single-molecule imaging, coherent Raman scattering microscopy, transient absorption microscopy, photothermal microscopy, ambient ionization mass spectrometry, electrochemical microscopy, and advanced chemical probes. These technologies have enabled significant breakthroughs in diagnosing pathological transitions, designing targeted therapies, and understanding drug resistance mechanisms. Recent advancements in resolution, contrast, sensitivity, and speed have transformed the field, with techniques like fluorescence, infrared absorption, and Raman scattering being widely applied across diverse biological domains. This review provides a comprehensive overview of the evolution and current state of chemical imaging technologies, coupled with systematic analyses of data processing workflows, including pre-processing, machine learning-assisted pattern extraction, and neural network-based predictions. Artificial intelligence (AI) and machine learning-assisted imaging are transforming chemical imaging through key advancements such as improved resolution and sensitivity
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.