Label-free optical microscopy with artificial intelligence: a new paradigm in pathology

SignificancePathological examination is essential for diagnosing diseases in tissues such as cancer but involves labor-intensive and time-consuming processes. Label-free optical microscopy has emerged as a promising alternative that offers the ability to visualize tissue structures without the need for histochemical staining. Further, the integration of artificial intelligence (AI) into label-free microscopy has the potential to streamline the overall pathological diagnostic process.AimWe aim to review the use of AI-assisted label-free optical microscopy in revolutionizing pathological workflows.ApproachWe examine the integration of AI with label-free optical microscopy techniques and assess its overall impact on the pathological workflow. We evaluate how AI enhances each stage of label-free pathology, including specimen preparation, label-free imaging, virtual staining, and diagnostic analysis.ResultsLabel-free optical microscopy with AI has significantly improved the entire pathological workflow. AI assists specimen preparation with high efficiency, enhances label-free imaging with high resolution and speed, and enables cost-effective virtual staining with high throughput and automatic diagnostic analysis with high accuracy.ConclusionsAI-aided label-free optical microscopy enhances diagnostic speed, accuracy, and specimen preservation, offering a transformative approach that could redefine traditional pathology workflows and improve clinical outcomes.

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Publication Details

Journal
Biophotonics discovery.
Published
2025-03-31
DOI
https://doi.org/10.1117/1.bios.2.2.020901
Citations
15
Primary Topic
Optical Coherence Tomography Applications
Type
article
Field-Weighted Citation Impact
10.11

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article

Label-free optical microscopy with artificial intelligence: a new paradigm in pathology

Chiho Yoon, Eunwoo Park, Byullee Park, Donggyu Kim
15 citations
Biophotonics discovery.
Optical Coherence Tomography Applications
10.11
article

Label-free optical microscopy with artificial intelligence: a new paradigm in pathology

Chiho Yoon, Eunwoo Park, Byullee Park, Donggyu Kim
article en
15 citations

Abstract

SignificancePathological examination is essential for diagnosing diseases in tissues such as cancer but involves labor-intensive and time-consuming processes. Label-free optical microscopy has emerged as a promising alternative that offers the ability to visualize tissue structures without the need for histochemical staining. Further, the integration of artificial intelligence (AI) into label-free microscopy has the potential to streamline the overall pathological diagnostic process.AimWe aim to review the use of AI-assisted label-free optical microscopy in revolutionizing pathological workflows.ApproachWe examine the integration of AI with label-free optical microscopy techniques and assess its overall impact on the pathological workflow. We evaluate how AI enhances each stage of label-free pathology, including specimen preparation, label-free imaging, virtual staining, and diagnostic analysis.ResultsLabel-free optical microscopy with AI has significantly improved the entire pathological workflow. AI assists specimen preparation with high efficiency, enhances label-free imaging with high resolution and speed, and enables cost-effective virtual staining with high throughput and automatic diagnostic analysis with high accuracy.ConclusionsAI-aided label-free optical microscopy enhances diagnostic speed, accuracy, and specimen preservation, offering a transformative approach that could redefine traditional pathology workflows and improve clinical outcomes.

Biophotonics discovery.Vol. 2(02)
Pohang University of Science and Technology (KR), Sungkyunkwan University (KR)
Ministry of Science and ICT, South Korea
Openalex Percentile: Top 1%
Optical Coherence Tomography Applications
10.11
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Label-free optical microscopy with artificial intelligence: a new paradigm in pathology — Chiho Yoon, Eunwoo Park, et al. · Biophotonics discovery. (2025) | TGRS Research Map | TGRS