Next‐Generation Pathology Unveiled: AI‐Enhanced Label‐Free Microimaging

Le résumé fourni par la source

ABSTRACT Pathological diagnosis is integral to disease detection, therapeutic decision‐making, and prognosis. Despite advances in digital pathology, current methods depend on chemically stained slides, which are labor‐intensive and time‐consuming. Label‐free microimaging techniques offer a promising alternative, capturing intrinsic physiological and structural information from biological tissues without chemical labeling or complex preparation. These modalities provide high‐resolution, nondestructive imaging of tissue architecture and pathology‐relevant biomarkers. However, the complexity of the instrumentation and difficulty of interpreting rich, multidimensional data pose significant barriers to clinical deployment. To address these challenges, artificial intelligence (AI)‐assisted methods, particularly deep learning, are being developed to reduce manual workloads and streamline pathology workflows. This review summarizes recent advancements in integrating label‐free optical imaging with AI in digital pathology. We highlight the role of deep learning models in enhancing image quality and automating pathological analysis. In addition, we discuss unresolved issues, such as limited model generalizability and clinical validation gaps, while suggesting future directions, including hardware innovations and foundation AI models. The integration of AI and label‐free microimaging is expected to advance digital pathology toward more intelligent, efficient, and precise diagnostics.

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