Pathology in the artificial intelligence era: practical insights for immunohistochemistry and molecular pathology

Artificial intelligence (AI) is driving a revolution in pathology, transforming traditional workflows and addressing critical challenges in the field. This review highlights the integration of AI into immunohistochemistry (IHC) and molecular pathology (MP), where its potential to enhance diagnostic accuracy, efficiency, and reproducibility is becoming increasingly evident. In IHC, AI tools offer solutions to limitations such as subjective biomarker scoring, interobserver variability, and growing workloads by enabling automated and consistent analysis of diagnostic and predictive markers. Similarly, in MP, AI addresses challenges in tumor annotation, genetic mutation interpretation and prediction, and integration of multidimensional data to streamline workflows and enhance precision medicine. By combining computational power with pathologists' expertise, AI holds the promise of reshaping pathology into a more efficient, reliable, and scalable discipline. However, continued efforts in validation, transparency, and cost optimization will be crucial to fully realize AI's transformative potential in clinical pathology.