Advances in molecular and digital pathology are fundamentally reshaping diagnostic medicine, translational research, and precision oncology. The integration of high-resolution tissue imaging, molecular profiling, and computational analysis has enabled pathologists to move beyond descriptive morphology toward data-rich, quantitative, and predictive interpretations of disease. As these approaches increasingly influence clinical decision-making and biomedical discovery, there is a timely need to consolidate emerging knowledge, conceptual frameworks, and applications across molecular and digital pathology.

Despite rapid advances, the field faces challenges, including variability in tissue processing, limited standardization, difficulties integrating molecular and imaging data, and obstacles to clinical translation, such as validation and regulatory approval. The growing use of AI and machine learning further raises concerns about robustness, bias, and reproducibility that must be addressed to ensure widespread adoption and trust.

This Topical Collection aims to capture the breadth and momentum of the field by featuring a curated mix of original research articles, methodological advances, and reviews that collectively reflect the evolving landscape of molecular and digital pathology. The collection seeks to emphasize biological insight, diagnostic and prognostic relevance, and translational impact, while showcasing how molecular data and digital analyses are being combined to enhance disease characterization. By highlighting both established and emerging applications, the issue will provide a coherent framework for understanding how these advances are shaping current practice and informing future innovation.

This collection will serve as a valuable resource for researchers, clinicians, and industry professionals. It will foster cross-disciplinary dialogue, promote best practices, and help accelerate the translation of molecular and digital pathology innovations into impactful research and improved patient care.