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Label-free optical microscopy with artificial intelligence: a new paradigm in pathology
Chiho Yoon1, Eunwoo Park1, Donggyu Kim1
1Pohang University of Science and Technology (POSTECH), Graduate School of Artificial Intelligence, and Medical Device Innovation Center, Departments of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering, Pohang, Republic of Korea.
Significance:
Pathological 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.
Aim:
We aim to review the use of AI-assisted label-free optical microscopy in revolutionizing pathological workflows.
Approach:
We 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.
Results:
Label-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.
Conclusions:
AI-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.

