Artificial Intelligence in Molecular Pathology: From Tumor Biology to Precision Diagnosis

Molecular pathology has entered a transformative era under the influence of artificial intelligence (AI). The intersection of digital pathology, molecular profiling, and computational algorithms is reshaping how tumor biology is interpreted and translated into clinical decision-making. Recent years have seen the emergence of AI-driven tools that enhance tumor classification, biomarker discovery, and risk stratification. These developments are particularly significant in the context of complex tumor ecosystems—marked by intratumoral heterogeneity, dynamic microenvironmental remodeling, and diverse signaling networks—that demand computational systems capable of integrating histopathological and molecular profiles. Nonetheless, challenges remain in ensuring model interpretability, biological relevance, and reproducibility across heterogeneous datasets. Addressing these issues will be critical for realizing the clinical promise of AI-based molecular diagnostics.This Research Topic aims to advance our understanding of how AI can connect tumor biology with precision diagnostics to support mechanism-aware molecular pathology. It seeks cutting-edge research that demonstrates how AI and machine learning can move beyond image pattern recognition toward biologically informed modeling and clinical translation. The collection encourages contributions that develop or apply AI tools for multi-omics integration, spatial biology, and tumor microenvironment analysis. Moreover, the Topic emphasizes the importance of explainable and trustworthy AI, translational implementation, and data standardization—key prerequisites for the adoption of AI in clinical pathology workflows.To gather further insights into the interdisciplinary frontier of AI and molecular pathology, we welcome contributions addressing, but not limited to, the following themes:• Integration of histopathology, genomics, transcriptomics, proteomics, and metabolomics for diagnostic modeling• AI-enabled biomarker discovery and molecular classification of tumors• Digital and computational pathology approaches for precision oncology• Multimodal and spatially resolved analysis of tumor heterogeneity and microenvironmental interactions• Development of explainable, interpretable, and trustworthy AI frameworks• Data management, harmonization, and standardization for robust diagnostic algorithms• Translational and clinical applications of AI in pathology and oncologyWe welcome Original Research, Review, Mini Review, Methods, and Perspective articles. Through this Research Topic, we aim to bring together experts from molecular pathology, cancer biology, bioinformatics, machine learning, and translational medicine to define the next frontier at the interface of artificial intelligence and biomedical diagnostics. Topic editors: Bonan Chen, Shihua Shi, Zhijian Huang Source: https://www.frontiersin.org/research-topics/81169/artificial-intelligence-in-molecular-pathology-from-tumor-biology-to-precision-diagnosis